{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T02:42:45Z","timestamp":1789094565893,"version":"build-2803163510"},"reference-count":64,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,2,12]],"date-time":"2025-02-12T00:00:00Z","timestamp":1739318400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,2,12]],"date-time":"2025-02-12T00:00:00Z","timestamp":1739318400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"China National Natural Science Foundation","doi-asserted-by":"crossref","award":["82374575"],"award-info":[{"award-number":["82374575"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004826","name":"Natural Science Foundation of Beijing Municipality","doi-asserted-by":"publisher","award":["7232270"],"award-info":[{"award-number":["7232270"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Capital\u2019s Funds for Health Improvement and Research","award":["CFH2024-2\u20132235"],"award-info":[{"award-number":["CFH2024-2\u20132235"]}]},{"name":"Out-standing Young Talents Program of Capital Medial University","award":["B2207"],"award-info":[{"award-number":["B2207"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Headache Pain"],"DOI":"10.1186\/s10194-025-01972-x","type":"journal-article","created":{"date-parts":[[2025,2,12]],"date-time":"2025-02-12T08:45:41Z","timestamp":1739349941000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Prediction models for treatment response in migraine: a systematic review and meta-analysis"],"prefix":"10.1186","volume":"26","author":[{"given":"Qiuyi","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiarun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baicheng","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yihan","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yazhuo","family":"Kong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,2,12]]},"reference":[{"issue":"4","key":"1972_CR1","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1016\/S1474-4422(24)00026-7","volume":"23","author":"HG Sutherland","year":"2024","unstructured":"Sutherland HG, Jenkins B, Griffiths LR (2024) Genetics of migraine: complexity, implications, and potential clinical applications. Lancet Neurol 23(4):429\u2013446. https:\/\/doi.org\/10.1016\/S1474-4422(24)00026-7","journal-title":"Lancet Neurol"},{"issue":"4","key":"1972_CR2","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1016\/S1474-4422(24)00038-3","volume":"23","author":"GBDNSD Collaborators","year":"2024","unstructured":"Collaborators GBDNSD (2024) Global, regional, and national burden of disorders affecting the nervous system, 1990\u20132021: a systematic analysis for the global burden of disease study 2021. Lancet Neurol 23(4):344\u2013381. https:\/\/doi.org\/10.1016\/S1474-4422(24)00038-3","journal-title":"Lancet Neurol"},{"issue":"9859","key":"1972_CR3","doi-asserted-by":"publisher","first-page":"2129","DOI":"10.1016\/S0140-6736(12)61680-8","volume":"380","author":"JA Salomon","year":"2012","unstructured":"Salomon JA, Vos T, Hogan DR, Gagnon M, Naghavi M, Mokdad A et al (2012) Common values in assessing health outcomes from disease and injury: disability weights measurement study for the global burden of disease study 2010. Lancet 380(9859):2129\u20132143. https:\/\/doi.org\/10.1016\/S0140-6736(12)61680-8","journal-title":"Lancet"},{"issue":"10283","key":"1972_CR4","doi-asserted-by":"publisher","first-page":"1505","DOI":"10.1016\/S0140-6736(20)32342-4","volume":"397","author":"M Ashina","year":"2021","unstructured":"Ashina M, Buse DC, Ashina H, Pozo-Rosich P, Peres MFP, Lee MJ et al (2021) Migraine: integrated approaches to clinical management and emerging treatments. Lancet 397(10283):1505\u20131518. https:\/\/doi.org\/10.1016\/S0140-6736(20)32342-4","journal-title":"Lancet"},{"issue":"5","key":"1972_CR5","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1001\/jamaneurol.2024.0368","volume":"81","author":"P Pozo-Rosich","year":"2024","unstructured":"Pozo-Rosich P, Dolezil D, Paemeleire K, Stepien A, Stude P, Snellman J et al (2024) Early use of erenumab vs nonspecific oral migraine preventives: the APPRAISE randomized clinical trial. JAMA Neurol 81(5):461\u2013470. https:\/\/doi.org\/10.1001\/jamaneurol.2024.0368","journal-title":"JAMA Neurol"},{"issue":"6","key":"1972_CR6","doi-asserted-by":"publisher","first-page":"333102424126108","DOI":"10.1177\/03331024241261080","volume":"44","author":"L Liu","year":"2024","unstructured":"Liu L, Chen Q, Zhao L, Lyu T, Nie L, Miao Q et al (2024) Acupuncture plus topiramate placebo versus topiramate plus sham acupuncture for the preventive treatment of chronic migraine: a single-blind, double-dummy, randomized controlled trial. Cephalalgia 44(6):3331024241261080. https:\/\/doi.org\/10.1177\/03331024241261080","journal-title":"Cephalalgia"},{"issue":"2","key":"1972_CR7","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.brs.2022.02.012","volume":"15","author":"H Hodaj","year":"2022","unstructured":"Hodaj H, Payen JF, Mick G, Vercueil L, Hodaj E, Dumolard A et al (2022) Long-term prophylactic efficacy of transcranial direct current stimulation in chronic migraine. A randomised, patient-assessor blinded, sham-controlled trial. Brain Stimul 15(2):441\u2013453. https:\/\/doi.org\/10.1016\/j.brs.2022.02.012","journal-title":"Brain Stimul"},{"key":"1972_CR8","doi-asserted-by":"publisher","first-page":"e080107","DOI":"10.1136\/bmj-2024-080107","volume":"386","author":"WK Karlsson","year":"2024","unstructured":"Karlsson WK, Ostinelli EG, Zhuang ZA, Kokoti L, Christensen RH, Al-Khazali HM et al (2024) Comparative effects of drug interventions for the acute management of migraine episodes in adults: systematic review and network meta-analysis. BMJ 386:e080107. https:\/\/doi.org\/10.1136\/bmj-2024-080107","journal-title":"BMJ"},{"issue":"9","key":"1972_CR9","doi-asserted-by":"publisher","first-page":"1094","DOI":"10.1111\/head.14806","volume":"64","author":"CC Chiang","year":"2024","unstructured":"Chiang CC, Schwedt TJ, Dumkrieger G, Wang L, Chao CJ, Ouellette HA et al (2024) Advancing toward precision migraine treatment: predicting responses to preventive medications with machine learning models based on patient and migraine features. Headache 64(9):1094\u20131108. https:\/\/doi.org\/10.1111\/head.14806","journal-title":"Headache"},{"key":"1972_CR10","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1016\/j.jclinepi.2023.06.003","volume":"159","author":"A Malmartel","year":"2023","unstructured":"Malmartel A, Ravaud P, Tran VT (2023) A methodological framework allows the identification of personomic markers to consider when designing personalized interventions. J Clin Epidemiol 159:235\u2013245. https:\/\/doi.org\/10.1016\/j.jclinepi.2023.06.003","journal-title":"J Clin Epidemiol"},{"issue":"34","key":"1972_CR11","doi-asserted-by":"publisher","first-page":"6236","DOI":"10.2174\/0929867325666180406115427","volume":"26","author":"A Russo","year":"2019","unstructured":"Russo A, Silvestro M, Tessitore A, Tedeschi G (2019) Functional neuroimaging biomarkers in migraine: diagnostic, prognostic and therapeutic implications. Curr Med Chem 26(34):6236\u20136252. https:\/\/doi.org\/10.2174\/0929867325666180406115427","journal-title":"Curr Med Chem"},{"issue":"1","key":"1972_CR12","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1002\/hbm.24372","volume":"40","author":"J Liu","year":"2019","unstructured":"Liu J, Mu J, Chen T, Zhang M, Tian J (2019) White matter tract microstructure of the mPFC-amygdala predicts interindividual differences in placebo response related to treatment in migraine patients. Hum Brain Mapp 40(1):284\u2013292. https:\/\/doi.org\/10.1002\/hbm.24372","journal-title":"Hum Brain Mapp"},{"issue":"9","key":"1972_CR13","doi-asserted-by":"publisher","first-page":"1143","DOI":"10.1177\/0333102419839975","volume":"39","author":"B Zhu","year":"2019","unstructured":"Zhu B, Coppola G, Shoaran M (2019) Migraine classification using somatosensory evoked potentials. Cephalalgia 39(9):1143\u20131155. https:\/\/doi.org\/10.1177\/0333102419839975","journal-title":"Cephalalgia"},{"key":"1972_CR14","doi-asserted-by":"publisher","first-page":"588207","DOI":"10.3389\/fneur.2020.588207","volume":"11","author":"T Yin","year":"2020","unstructured":"Yin T, Sun G, Tian Z, Liu M, Gao Y, Dong M et al (2020) The spontaneous activity pattern of the middle occipital gyrus predicts the clinical efficacy of acupuncture treatment for migraine without aura. Front Neurol 11:588207. https:\/\/doi.org\/10.3389\/fneur.2020.588207","journal-title":"Front Neurol"},{"key":"1972_CR15","doi-asserted-by":"publisher","first-page":"103168","DOI":"10.1016\/j.nicl.2022.103168","volume":"36","author":"L Liu","year":"2022","unstructured":"Liu L, Lyu TL, Fu MY, Wang LP, Chen Y, Hong JH et al (2022) Changes in brain connectivity linked to multisensory processing of pain modulation in migraine with acupuncture treatment. Neuroimage Clin 36:103168. https:\/\/doi.org\/10.1016\/j.nicl.2022.103168","journal-title":"Neuroimage Clin"},{"issue":"1","key":"1972_CR16","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1186\/s10194-024-01812-4","volume":"25","author":"HL Wei","year":"2024","unstructured":"Wei HL, Yu YS, Wang MY, Zhou GP, Li J, Zhang H et al (2024) Exploring potential neuroimaging biomarkers for the response to non-steroidal anti-inflammatory drugs in episodic migraine. J Headache Pain 25(1):104. https:\/\/doi.org\/10.1186\/s10194-024-01812-4","journal-title":"J Headache Pain"},{"issue":"1","key":"1972_CR17","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1186\/s12874-019-0681-4","volume":"19","author":"JAM Sidey-Gibbons","year":"2019","unstructured":"Sidey-Gibbons JAM, Sidey-Gibbons CJ (2019) Machine learning in medicine: a practical introduction. BMC Med Res Methodol 19(1):64. https:\/\/doi.org\/10.1186\/s12874-019-0681-4","journal-title":"BMC Med Res Methodol"},{"issue":"4","key":"1972_CR18","doi-asserted-by":"publisher","first-page":"e342","DOI":"10.1212\/WNL.0000000000006822","volume":"92","author":"MJ Lee","year":"2019","unstructured":"Lee MJ, Park BY, Cho S, Park H, Chung CS (2019) Cerebrovascular reactivity as a determinant of deep white matter hyperintensities in migraine. Neurology 92(4):e342\u2013e50. https:\/\/doi.org\/10.1212\/WNL.0000000000006822","journal-title":"Neurology"},{"key":"1972_CR19","doi-asserted-by":"publisher","first-page":"1487","DOI":"10.1016\/j.csbj.2020.06.006","volume":"18","author":"P Ferroni","year":"2020","unstructured":"Ferroni P, Zanzotto FM, Scarpato N, Spila A, Fofi L, Egeo G et al (2020) Machine learning approach to predict medication overuse in migraine patients. Comput Struct Biotechnol J 18:1487\u20131496. https:\/\/doi.org\/10.1016\/j.csbj.2020.06.006","journal-title":"Comput Struct Biotechnol J"},{"issue":"1","key":"1972_CR20","doi-asserted-by":"publisher","first-page":"14705","DOI":"10.1038\/s41598-024-65613-0","volume":"14","author":"Z Zhao","year":"2024","unstructured":"Zhao Z, Zhao M, Yang T, Li J, Qin C, Wang B et al (2024) Identifying significant structural factors associated with knee pain severity in patients with osteoarthritis using machine learning. Sci Rep 14(1):14705. https:\/\/doi.org\/10.1038\/s41598-024-65613-0","journal-title":"Sci Rep"},{"issue":"1","key":"1972_CR21","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1186\/s10194-024-01861-9","volume":"25","author":"W Chen","year":"2024","unstructured":"Chen W, Zhao H, Feng Q, Xiong X, Ke J, Dai L et al (2024) Disrupted gray matter connectome in vestibular migraine: a combined machine learning and individual-level morphological brain network analysis. J Headache Pain 25(1):177. https:\/\/doi.org\/10.1186\/s10194-024-01861-9","journal-title":"J Headache Pain"},{"key":"1972_CR22","doi-asserted-by":"publisher","first-page":"120253","DOI":"10.1016\/j.neuroimage.2023.120253","volume":"277","author":"P Tholke","year":"2023","unstructured":"Tholke P, Mantilla-Ramos YJ, Abdelhedi H, Maschke C, Dehgan A, Harel Y et al (2023) Class imbalance should not throw you off balance: choosing the right classifiers and performance metrics for brain decoding with imbalanced data. NeuroImage 277:120253. https:\/\/doi.org\/10.1016\/j.neuroimage.2023.120253","journal-title":"NeuroImage"},{"key":"1972_CR23","doi-asserted-by":"publisher","first-page":"778139","DOI":"10.3389\/fnmol.2022.778139","volume":"15","author":"M Feng","year":"2022","unstructured":"Feng M, Zhang Y, Wen Z, Hou X, Ye Y, Fu C et al (2022) Early fractional amplitude of low frequency fluctuation can predict the efficacy of transcutaneous auricular vagus nerve stimulation treatment for migraine without aura. Front Mol Neurosci 15:778139. https:\/\/doi.org\/10.3389\/fnmol.2022.778139","journal-title":"Front Mol Neurosci"},{"issue":"10","key":"1972_CR24","doi-asserted-by":"publisher","first-page":"3102","DOI":"10.1111\/ene.15458","volume":"29","author":"A Gonzalez-Martinez","year":"2022","unstructured":"Gonzalez-Martinez A, Pag\u00e1n J, Sanz\u2010Garc\u00eda A, Garc\u00eda\u2010Azor\u00edn D, Rodr\u00edguez\u2010Vico JS, Jaimes A et al (2022) Machine\u2010learning\u2010based approach for predicting response to anti\u2010calcitonin gene\u2010related peptide (CGRP) receptor or ligand antibody treatment in patients with migraine: a multicenter Spanish study. Eur J Neurol 29(10):3102\u20133111. https:\/\/doi.org\/10.1111\/ene.15458","journal-title":"Eur J Neurol"},{"key":"1972_CR25","doi-asserted-by":"publisher","first-page":"1002080","DOI":"10.3389\/fphar.2022.1002080","volume":"13","author":"Z-X Lu","year":"2022","unstructured":"Lu Z-X, Dong B-Q, Wei H-L, Chen L (2022) Prediction and associated factors of non-steroidal anti-inflammatory drugs efficacy in migraine treatment. Front Pharmacol 13:1002080. https:\/\/doi.org\/10.3389\/fphar.2022.1002080","journal-title":"Front Pharmacol"},{"issue":"10","key":"1972_CR26","doi-asserted-by":"publisher","first-page":"e1001744","DOI":"10.1371\/journal.pmed.1001744","volume":"11","author":"KG Moons","year":"2014","unstructured":"Moons KG, de Groot JA, Bouwmeester W, Vergouwe Y, Mallett S, Altman DG et al (2014) Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS Med 11(10):e1001744. https:\/\/doi.org\/10.1371\/journal.pmed.1001744","journal-title":"PLoS Med"},{"key":"1972_CR27","doi-asserted-by":"publisher","first-page":"i6460","DOI":"10.1136\/bmj.i6460","volume":"356","author":"TP Debray","year":"2017","unstructured":"Debray TP, Damen JA, Snell KI, Ensor J, Hooft L, Reitsma JB et al (2017) A guide to systematic review and meta-analysis of prediction model performance. BMJ 356:i6460. https:\/\/doi.org\/10.1136\/bmj.i6460","journal-title":"BMJ"},{"issue":"1","key":"1972_CR28","doi-asserted-by":"publisher","first-page":"51","DOI":"10.7326\/M18-1376","volume":"170","author":"RF Wolff","year":"2019","unstructured":"Wolff RF, Moons KGM, Riley RD, Whiting PF, Westwood M, Collins GS et al (2019) PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med 170(1):51\u201358. https:\/\/doi.org\/10.7326\/M18-1376","journal-title":"Ann Intern Med"},{"issue":"1","key":"1972_CR29","doi-asserted-by":"publisher","first-page":"W1","DOI":"10.7326\/M18-1377","volume":"170","author":"KGM Moons","year":"2019","unstructured":"Moons KGM, Wolff RF, Riley RD, Whiting PF, Westwood M, Collins GS et al (2019) PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration. Ann Intern Med 170(1):W1\u2013W33. https:\/\/doi.org\/10.7326\/M18-1377","journal-title":"Ann Intern Med"},{"key":"1972_CR30","doi-asserted-by":"publisher","first-page":"e078378","DOI":"10.1136\/bmj-2023-078378","volume":"385","author":"GS Collins","year":"2024","unstructured":"Collins GS, Moons KGM, Dhiman P, Riley RD, Beam AL, Van Calster B et al (2024) TRIPOD\u2009+\u2009AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ 385:e078378. https:\/\/doi.org\/10.1136\/bmj-2023-078378","journal-title":"BMJ"},{"issue":"17","key":"1972_CR31","doi-asserted-by":"publisher","first-page":"2693","DOI":"10.1002\/sim.1482","volume":"22","author":"G Knapp","year":"2003","unstructured":"Knapp G, Hartung J (2003) Improved tests for a random effects meta-regression with a single covariate. Stat Med 22(17):2693\u20132710. https:\/\/doi.org\/10.1002\/sim.1482","journal-title":"Stat Med"},{"issue":"1","key":"1972_CR32","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1002\/jrsm.1316","volume":"10","author":"D Langan","year":"2019","unstructured":"Langan D, Higgins JPT, Jackson D, Bowden J, Veroniki AA, Kontopantelis E et al (2019) A comparison of heterogeneity variance estimators in simulated random-effects meta-analyses. Res Synth Methods 10(1):83\u201398. https:\/\/doi.org\/10.1002\/jrsm.1316","journal-title":"Res Synth Methods"},{"issue":"7414","key":"1972_CR33","doi-asserted-by":"publisher","first-page":"557","DOI":"10.1136\/bmj.327.7414.557","volume":"327","author":"JP Higgins","year":"2003","unstructured":"Higgins JP, Thompson SG, Deeks JJ, Altman DG (2003) Measuring inconsistency in meta-analyses. BMJ 327(7414):557\u2013560. https:\/\/doi.org\/10.1136\/bmj.327.7414.557","journal-title":"BMJ"},{"key":"1972_CR34","unstructured":"R Core Team (2019) R: A language and environment for statistical computing"},{"issue":"15","key":"1972_CR35","doi-asserted-by":"publisher","first-page":"3533","DOI":"10.1002\/sim.8981","volume":"40","author":"VMT de Jong","year":"2021","unstructured":"de Jong VMT, Moons KGM, Eijkemans MJC, Riley RD, Debray TPA (2021) Developing more generalizable prediction models from pooled studies and large clustered data sets. Stat Med 40(15):3533\u20133559. https:\/\/doi.org\/10.1002\/sim.8981","journal-title":"Stat Med"},{"key":"1972_CR36","doi-asserted-by":"publisher","first-page":"n71","DOI":"10.1136\/bmj.n71","volume":"372","author":"MJ Page","year":"2021","unstructured":"Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD et al (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372:n71. https:\/\/doi.org\/10.1136\/bmj.n71","journal-title":"BMJ"},{"key":"1972_CR37","doi-asserted-by":"publisher","first-page":"819507","DOI":"10.3389\/fnmol.2022.819507","volume":"15","author":"H-L Wei","year":"2022","unstructured":"Wei H-L, Xu C-H, Wang J-J, Zhou G-P, Guo X, Chen Y-C et al (2022) Disrupted functional connectivity of the Amygdala predicts the efficacy of non-steroidal anti-inflammatory drugs in migraineurs without Aura. Front Mol Neurosci 15:819507. https:\/\/doi.org\/10.3389\/fnmol.2022.819507","journal-title":"Front Mol Neurosci"},{"issue":"3","key":"1972_CR38","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1111\/ejn.16219","volume":"59","author":"HL Wei","year":"2024","unstructured":"Wei HL, Yang Q, Zhou GP, Chen YC, Yu YS, Yin X et al (2024) Abnormal causal connectivity of anterior cingulate cortex-visual cortex circuit related to nonsteroidal anti\u2010inflammatory drug efficacy in migraine. Eur J Neurosci 59(3):446\u2013456. https:\/\/doi.org\/10.1111\/ejn.16219","journal-title":"Eur J Neurosci"},{"key":"1972_CR39","doi-asserted-by":"publisher","first-page":"111","DOI":"10.3389\/fneur.2020.00111","volume":"11","author":"X-J Yang","year":"2020","unstructured":"Yang X-J, Liu L, Xu Z-L, Zhang Y-J, Liu D-P, Fishers M et al (2020) Baseline brain Gray Matter volume as a predictor of acupuncture outcome in treating migraine. Front Neurol 11:111. https:\/\/doi.org\/10.3389\/fneur.2020.00111","journal-title":"Front Neurol"},{"key":"1972_CR40","doi-asserted-by":"publisher","first-page":"798695","DOI":"10.3389\/fneur.2022.798695","volume":"13","author":"J-W Wu","year":"2022","unstructured":"Wu J-W, Lai P-Y, Chen Y-L, Wang Y-F, Lirng J-F, Chen S-T et al (2022) The use of neuroimaging for predicting sumatriptan treatment response in patients with migraine. Front Neurol 13:798695. https:\/\/doi.org\/10.3389\/fneur.2022.798695","journal-title":"Front Neurol"},{"issue":"1","key":"1972_CR41","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1186\/s10194-024-01874-4","volume":"25","author":"T Aramruang","year":"2024","unstructured":"Aramruang T, Malhotra A, Numthavaj P, Looareesuwan P, Anothaisintawee T, Dejthevaporn C et al (2024) Prediction models for identifying medication overuse or medication overuse headache in migraine patients: a systematic review. J Headache Pain 25(1):165. https:\/\/doi.org\/10.1186\/s10194-024-01874-4","journal-title":"J Headache Pain"},{"key":"1972_CR42","doi-asserted-by":"publisher","first-page":"204062232110652","DOI":"10.1177\/20406223211065235","volume":"13","author":"YW Woldeamanuel","year":"2022","unstructured":"Woldeamanuel YW, Cowan RP (2022) Computerized migraine diagnostic tools: a systematic review. Ther Adv Chronic Dis 13:20406223211065235. https:\/\/doi.org\/10.1177\/20406223211065235","journal-title":"Ther Adv Chronic Dis"},{"issue":"12","key":"1972_CR43","doi-asserted-by":"publisher","first-page":"1179","DOI":"10.1016\/S1474-4422(24)00427-7","volume":"23","author":"M Leone","year":"2024","unstructured":"Leone M (2024) Globalisation of the pharmacological treatment of migraine. Lancet Neurol 23(12):1179\u20131180. https:\/\/doi.org\/10.1016\/S1474-4422(24)00427-7","journal-title":"Lancet Neurol"},{"issue":"1","key":"1972_CR44","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1038\/s41398-021-01286-x","volume":"11","author":"SE Cohen","year":"2021","unstructured":"Cohen SE, Zantvoord JB, Wezenberg BN, Bockting CLH, van Wingen GA (2021) Magnetic resonance imaging for individual prediction of treatment response in major depressive disorder: a systematic review and meta-analysis. Transl Psychiatry 11(1):168. https:\/\/doi.org\/10.1038\/s41398-021-01286-x","journal-title":"Transl Psychiatry"},{"key":"1972_CR45","doi-asserted-by":"publisher","first-page":"102193","DOI":"10.1016\/j.cpr.2022.102193","volume":"97","author":"S Vieira","year":"2022","unstructured":"Vieira S, Liang X, Guiomar R, Mechelli A (2022) Can we predict who will benefit from cognitive-behavioural therapy? A systematic review and meta-analysis of machine learning studies. Clin Psychol Rev 97:102193. https:\/\/doi.org\/10.1016\/j.cpr.2022.102193","journal-title":"Clin Psychol Rev"},{"key":"1972_CR46","doi-asserted-by":"publisher","first-page":"105640","DOI":"10.1016\/j.neubiorev.2024.105640","volume":"160","author":"C Meinke","year":"2024","unstructured":"Meinke C, Lueken U, Walter H, Hilbert K (2024) Predicting treatment outcome based on resting-state functional connectivity in internalizing mental disorders: a systematic review and meta-analysis. Neurosci Biobehav Rev 160:105640. https:\/\/doi.org\/10.1016\/j.neubiorev.2024.105640","journal-title":"Neurosci Biobehav Rev"},{"issue":"1","key":"1972_CR47","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1186\/s10194-020-01086-6","volume":"21","author":"D Zhang","year":"2020","unstructured":"Zhang D, Huang X, Su W, Chen Y, Wang P, Mao C et al (2020) Altered lateral geniculate nucleus functional connectivity in migraine without aura: a resting-state functional MRI study. J Headache Pain 21(1):17. https:\/\/doi.org\/10.1186\/s10194-020-01086-6","journal-title":"J Headache Pain"},{"issue":"1","key":"1972_CR48","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1186\/s10194-023-01617-x","volume":"24","author":"N Karsan","year":"2023","unstructured":"Karsan N, Goadsby PJ (2023) Neuroimaging in the pre-ictal or premonitory phase of migraine: a narrative review. J Headache Pain 24(1):106. https:\/\/doi.org\/10.1186\/s10194-023-01617-x","journal-title":"J Headache Pain"},{"issue":"2","key":"1972_CR49","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1097\/CCM.0000000000001571","volume":"44","author":"MM Churpek","year":"2016","unstructured":"Churpek MM, Yuen TC, Winslow C, Meltzer DO, Kattan MW, Edelson DP (2016) Multicenter comparison of machine learning methods and conventional regression for predicting clinical deterioration on the wards. Crit Care Med 44(2):368\u2013374. https:\/\/doi.org\/10.1097\/CCM.0000000000001571","journal-title":"Crit Care Med"},{"issue":"1","key":"1972_CR50","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1186\/s12874-024-02268-5","volume":"24","author":"M Pavlou","year":"2024","unstructured":"Pavlou M, Ambler G, Qu C, Seaman SR, White IR, Omar RZ (2024) An evaluation of sample size requirements for developing risk prediction models with binary outcomes. BMC Med Res Methodol 24(1):146. https:\/\/doi.org\/10.1186\/s12874-024-02268-5","journal-title":"BMC Med Res Methodol"},{"issue":"1","key":"1972_CR51","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1038\/s41746-024-01360-w","volume":"7","author":"K Zantvoort","year":"2024","unstructured":"Zantvoort K, Nacke B, Gorlich D, Hornstein S, Jacobi C, Funk B (2024) Estimation of minimal data sets sizes for machine learning predictions in digital mental health interventions. NPJ Digit Med 7(1):361. https:\/\/doi.org\/10.1038\/s41746-024-01360-w","journal-title":"NPJ Digit Med"},{"issue":"5","key":"1972_CR52","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/s0895-4356(03)00047-7","volume":"56","author":"EW Steyerberg","year":"2003","unstructured":"Steyerberg EW, Bleeker SE, Moll HA, Grobbee DE, Moons KG (2003) Internal and external validation of predictive models: a simulation study of bias and precision in small samples. J Clin Epidemiol 56(5):441\u2013447. https:\/\/doi.org\/10.1016\/s0895-4356(03)00047-7","journal-title":"J Clin Epidemiol"},{"issue":"5","key":"1972_CR53","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1016\/j.jclinepi.2004.06.017","volume":"58","author":"Y Vergouwe","year":"2005","unstructured":"Vergouwe Y, Steyerberg EW, Eijkemans MJ, Habbema JD (2005) Substantial effective sample sizes were required for external validation studies of predictive logistic regression models. J Clin Epidemiol 58(5):475\u2013483. https:\/\/doi.org\/10.1016\/j.jclinepi.2004.06.017","journal-title":"J Clin Epidemiol"},{"issue":"19","key":"1972_CR54","doi-asserted-by":"publisher","first-page":"4230","DOI":"10.1002\/sim.9025","volume":"40","author":"RD Riley","year":"2021","unstructured":"Riley RD, Debray TPA, Collins GS, Archer L, Ensor J, van Smeden M et al (2021) Minimum sample size for external validation of a clinical prediction model with a binary outcome. Stat Med 40(19):4230\u20134251. https:\/\/doi.org\/10.1002\/sim.9025","journal-title":"Stat Med"},{"issue":"16","key":"1972_CR55","doi-asserted-by":"publisher","first-page":"2742","DOI":"10.1017\/S0033291721003871","volume":"51","author":"M Sajjadian","year":"2021","unstructured":"Sajjadian M, Lam RW, Milev R, Rotzinger S, Frey BN, Soares CN et al (2021) Machine learning in the prediction of depression treatment outcomes: a systematic review and meta-analysis. Psychol Med 51(16):2742\u20132751. https:\/\/doi.org\/10.1017\/S0033291721003871","journal-title":"Psychol Med"},{"key":"1972_CR56","doi-asserted-by":"publisher","unstructured":"Barboza F, Kimura H, Altman E (2017) Machine learning models and bankruptcy prediction. Expert systems with Applications 83:405\u201317. https:\/\/doi.org\/10.1016\/j.eswa.2017.04.006","DOI":"10.1016\/j.eswa.2017.04.006"},{"issue":"1","key":"1972_CR57","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1186\/s12874-022-01758-8","volume":"22","author":"A Pfob","year":"2022","unstructured":"Pfob A, Lu SC, Sidey-Gibbons C (2022) Machine learning in medicine: a practical introduction to techniques for data pre-processing, hyperparameter tuning, and model comparison. BMC Med Res Methodol 22(1):282. https:\/\/doi.org\/10.1186\/s12874-022-01758-8","journal-title":"BMC Med Res Methodol"},{"issue":"4","key":"1972_CR58","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1007\/s10916-017-0715-6","volume":"41","author":"HO Alanazi","year":"2017","unstructured":"Alanazi HO, Abdullah AH, Qureshi KN (2017) A critical review for developing accurate and dynamic predictive models using machine learning methods in medicine and health care. J Med Syst 41(4):69. https:\/\/doi.org\/10.1007\/s10916-017-0715-6","journal-title":"J Med Syst"},{"issue":"3","key":"1972_CR59","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1007\/s10664-022-10126-5","volume":"27","author":"F Pecorelli","year":"2022","unstructured":"Pecorelli F, Lujan S, Lenarduzzi V, Palomba F, De Lucia A (2022) On the adequacy of static analysis warnings with respect to code smell prediction. Empir Softw Eng 27(3):64. https:\/\/doi.org\/10.1007\/s10664-022-10126-5","journal-title":"Empir Softw Eng"},{"key":"1972_CR60","doi-asserted-by":"crossref","unstructured":"Boutron I, Page MJ, Higgins JP, Altman DG, Lundh A, Hr\u00f3bjartsson A et al (2019) Considering bias and conflicts of interest among the included studies. Cochrane Handbook for Systematic Reviews of Interventions 177\u2013204","DOI":"10.1002\/9781119536604.ch7"},{"key":"1972_CR61","doi-asserted-by":"publisher","first-page":"b2393","DOI":"10.1136\/bmj.b2393","volume":"338","author":"JA Sterne","year":"2009","unstructured":"Sterne JA, White IR, Carlin JB, Spratt M, Royston P, Kenward MG et al (2009) Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ 338:b2393. https:\/\/doi.org\/10.1136\/bmj.b2393","journal-title":"BMJ"},{"issue":"2","key":"1972_CR62","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1016\/j.jclinepi.2009.03.017","volume":"63","author":"Y Vergouwe","year":"2010","unstructured":"Vergouwe Y, Royston P, Moons KG, Altman DG (2010) Development and validation of a prediction model with missing predictor data: a practical approach. J Clin Epidemiol 63(2):205\u2013214. https:\/\/doi.org\/10.1016\/j.jclinepi.2009.03.017","journal-title":"J Clin Epidemiol"},{"key":"1972_CR63","doi-asserted-by":"publisher","first-page":"g7594","DOI":"10.1136\/bmj.g7594","volume":"350","author":"GS Collins","year":"2015","unstructured":"Collins GS, Reitsma JB, Altman DG, Moons KG (2015) Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ 350:g7594. https:\/\/doi.org\/10.1136\/bmj.g7594","journal-title":"BMJ"},{"issue":"8","key":"1972_CR64","doi-asserted-by":"publisher","first-page":"774","DOI":"10.1016\/s0895-4356(01)00341-9","volume":"54","author":"EW Steyerberg","year":"2001","unstructured":"Steyerberg EW, Harrell FE Jr., Borsboom GJ, Eijkemans MJ, Vergouwe Y, Habbema JD (2001) Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. J Clin Epidemiol 54(8):774\u2013781. https:\/\/doi.org\/10.1016\/s0895-4356(01)00341-9","journal-title":"J Clin Epidemiol"}],"container-title":["The Journal of Headache and Pain"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s10194-025-01972-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s10194-025-01972-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s10194-025-01972-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,13]],"date-time":"2025-02-13T15:50:26Z","timestamp":1739461826000},"score":1,"resource":{"primary":{"URL":"https:\/\/thejournalofheadacheandpain.biomedcentral.com\/articles\/10.1186\/s10194-025-01972-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,12]]},"references-count":64,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1972"],"URL":"https:\/\/doi.org\/10.1186\/s10194-025-01972-x","relation":{},"ISSN":["1129-2377"],"issn-type":[{"value":"1129-2377","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,12]]},"assertion":[{"value":"10 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 February 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 February 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human ethics and consent to participate"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"32"}}