{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T17:47:26Z","timestamp":1790704046136,"version":"4.1.0"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T00:00:00Z","timestamp":1769644800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T00:00:00Z","timestamp":1769644800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"the Key Project of the Jinhua Science and Technology Bureau","award":["Grant No. 2024-3-115"],"award-info":[{"award-number":["Grant No. 2024-3-115"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Gastroenterol"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Colorectal cancer (CRC) is one of the most prevalent malignant diseases worldwide and displays significant heterogeneity. The aim of this study was to investigate the application of machine learning algorithms to incorporate preoperative laboratory tests for predicting the 5-year recurrence risk in patients with stage III colon cancer (CC) postsurgery.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This study included two patient cohorts: the Zhejiang Cancer Hospital CC cohort (ZCC set,\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009290), which served as the training cohort, and the Dongyang CC cohort (DYC set,\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009125), which was utilized as an external testing cohort. Univariate analysis was initially performed on the 48 preoperative laboratory tests and 15 clinical and pathological features within the training cohort to pinpoint potential predictors. Features with a p value less than 0.05 were incorporated, and six machine learning models\u2014logistic regression, random forest, XGBoost, support vector machine (SVM), back propagation neural network (BP NET), and K-nearest neighbour (KNN)\u2014were employed to develop a model for predicting the 5-year recurrence risk in patients with stage III colon cancer. The prediction efficacy was assessed by calculating the area under the curve (AUC) of the machine learning model using the external test dataset, and comparisons were performed via the DeLong test. Ultimately, the Shapley additive explanations (SHAP) algorithm was applied to rank feature importance and compute the SHAP values for each feature, which were then visualized.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Univariate analysis identified 10 laboratory tests and 6 clinical and pathological features that were incorporated into six machine learning models. The random forest model exhibited the highest predictive performance in the test cohort, with an AUC of 0.845. Logistic regression closely trailed, achieving an AUC of 0.823. The DeLong test revealed that the predictive performance of the random forest model was comparable to that of logistic regression and outperformed the other models. SHAP analysis indicated that the most important feature for predicting the 5-year recurrence risk of stage III colon cancer was perineural invasion, followed by FIB and then PT.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>A machine learning model constructed using preoperative laboratory tests and clinical and pathological features can assist in predicting the 5-year recurrence risk of patients with stage III colon cancer. This model provides potential reference values for the clinical development of individualized treatment strategies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12876-025-04511-9","type":"journal-article","created":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T13:55:48Z","timestamp":1769694948000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Interpretable machine learning model for predicting 5-Year postoperative recurrence risk in patients with stage III colon cancer using preoperative laboratory tests: a two-centre study"],"prefix":"10.1186","volume":"26","author":[{"given":"Hangping","family":"Wei","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xihao","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanyuan","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinkai","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"ZhenXin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,29]]},"reference":[{"issue":"2","key":"4511_CR1","doi-asserted-by":"publisher","first-page":"338","DOI":"10.1136\/gutjnl-2022-327736","volume":"72","author":"E Morgan","year":"2023","unstructured":"Morgan E, Arnold M, Gini A, et al. Global burden of colorectal cancer in 2020 and 2040: incidence and mortality estimates from GLOBOCAN. Gut. 2023;72(2):338\u201344.","journal-title":"Gut"},{"issue":"3","key":"4511_CR2","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1016\/S2468-1253(21)00426-X","volume":"7","author":"SG Patel","year":"2022","unstructured":"Patel SG, Karlitz JJ, Yen T, et al. The rising tide of early-onset colorectal cancer: a comprehensive review of epidemiology, clinical features, biology, risk factors, prevention, and early detection. Lancet Gastroenterol Hepatol. 2022;7(3):262\u201374.","journal-title":"Lancet Gastroenterol Hepatol"},{"issue":"3","key":"4511_CR3","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1002\/med4.32","volume":"1","author":"BT Oggesen","year":"2023","unstructured":"Oggesen BT, Hamberg MLS, Rosenberg J. Practical management algorithms for late complications after colorectal and anal cancer\u2014Basic treatment of late complications. Med Adv. 2023;1(3):260\u20139.","journal-title":"Med Adv"},{"issue":"9","key":"4511_CR4","doi-asserted-by":"publisher","first-page":"1136","DOI":"10.5306\/wjco.v15.i9.1136","volume":"15","author":"H Fadlallah","year":"2024","unstructured":"Fadlallah H, El Masri J, Fakhereddine H, et al. Colorectal cancer: recent advances in management and treatment. World J Clin Oncol. 2024;15(9):1136\u201356.","journal-title":"World J Clin Oncol"},{"issue":"3","key":"4511_CR5","first-page":"198","volume":"21","author":"JC Fabregas","year":"2022","unstructured":"Fabregas JC, Ramnaraign B, George TJ. Clinical updates for colon cancer care in 2022. Clin Colorectal Cancer. 2022;21(3):198\u2013203.","journal-title":"Clin Colorectal Cancer"},{"issue":"5","key":"4511_CR6","doi-asserted-by":"publisher","first-page":"3049","DOI":"10.1245\/s10434-025-17043-z","volume":"32","author":"T Dhimal","year":"2025","unstructured":"Dhimal T, Fleming FJ. Neoadjuvant immunotherapy in operable colon cancer: opportunities and challenges. Ann Surg Oncol. 2025;32(5):3049\u201351.","journal-title":"Ann Surg Oncol"},{"key":"4511_CR7","unstructured":"Benson AB, Venook AP, Adam M et al. Colon Cancer, version 3.2024, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw. 2024;22(2 D)."},{"issue":"9","key":"4511_CR8","doi-asserted-by":"publisher","first-page":"1106","DOI":"10.1080\/0284186X.2021.1943519","volume":"60","author":"L Gately","year":"2021","unstructured":"Gately L, Jalali A, Semira C, et al. Stage dependent recurrence patterns and post-recurrence outcomes in non-metastatic colon cancer. Acta Oncol. 2021;60(9):1106\u201313.","journal-title":"Acta Oncol"},{"key":"4511_CR9","doi-asserted-by":"crossref","unstructured":"Ryu HS, Kim J, Park YR et al. Recurrence patterns and risk factors after curative resection for colorectal cancer: insights for postoperative surveillance strategies. Cancers (Basel). 2023;15(24).","DOI":"10.3390\/cancers15245791"},{"issue":"3","key":"4511_CR10","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1158\/1078-0432.CCR-21-2404","volume":"28","author":"TV Henriksen","year":"2022","unstructured":"Henriksen TV, Tarazona N, Frydendahl A, et al. Circulating tumor DNA in stage III colorectal Cancer, beyond minimal residual disease Detection, toward assessment of adjuvant therapy efficacy and clinical behavior of recurrences. Clin Cancer Res. 2022;28(3):507\u201317.","journal-title":"Clin Cancer Res"},{"issue":"5","key":"4511_CR11","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1001\/jamasurg.2023.8015","volume":"159","author":"W Jiang","year":"2024","unstructured":"Jiang W, Wang H, Dong X, et al. Pathomics signature for prognosis and chemotherapy benefits in stage III colon cancer. JAMA Surg. 2024;159(5):519\u201328.","journal-title":"JAMA Surg"},{"issue":"1","key":"4511_CR12","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1001\/jamaoncol.2023.5098","volume":"10","author":"J Nors","year":"2024","unstructured":"Nors J, Iversen LH, Erichsen R, et al. Incidence of recurrence and time to recurrence in stage I to III colorectal cancer: A nationwide Danish cohort study. JAMA Oncol. 2024;10(1):54\u201362.","journal-title":"JAMA Oncol"},{"issue":"1","key":"4511_CR13","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1002\/med4.9","volume":"1","author":"H Ye","year":"2023","unstructured":"Ye H, Ye Y, Wang Y, et al. Automated assessment of necrosis tumor ratio in colorectal cancer using an artificial intelligence-based digital pathology analysis. Med Adv. 2023;1(1):30\u201343.","journal-title":"Med Adv"},{"issue":"8","key":"4511_CR14","doi-asserted-by":"publisher","first-page":"101661","DOI":"10.1016\/j.xcrm.2024.101661","volume":"5","author":"C Xu","year":"2024","unstructured":"Xu C, Xia P, Li J, et al. Discovery and validation of a 10-gene predictive signature for response to adjuvant chemotherapy in stage II and III colon cancer. Cell Rep Med. 2024;5(8):101661.","journal-title":"Cell Rep Med"},{"key":"4511_CR15","doi-asserted-by":"crossref","unstructured":"Muhammad D, Ahmed I, Ahmad MO et al. Randomized explainable machine learning models for efficient medical diagnosis. IEEE J Biomed Health Inf. 2024;PP.","DOI":"10.1109\/JBHI.2024.3491593"},{"issue":"1","key":"4511_CR16","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1038\/s41580-021-00407-0","volume":"23","author":"JG Greener","year":"2022","unstructured":"Greener JG, Kandathil SM, Moffat L, et al. A guide to machine learning for biologists. Nat Rev Mol Cell Biol. 2022;23(1):40\u201355.","journal-title":"Nat Rev Mol Cell Biol"},{"key":"4511_CR17","doi-asserted-by":"publisher","first-page":"558162","DOI":"10.3389\/fonc.2020.558162","volume":"10","author":"J Yan","year":"2020","unstructured":"Yan J, Liu L, Wang W, et al. Radiomic features from Multi-Parameter MRI combined with clinical parameters predict molecular subgroups in patients with Medulloblastoma. Front Oncol. 2020;10:558162.","journal-title":"Front Oncol"},{"issue":"1","key":"4511_CR18","doi-asserted-by":"publisher","first-page":"848","DOI":"10.1186\/s12885-023-11338-8","volume":"23","author":"Z Liu","year":"2023","unstructured":"Liu Z, Hong X, Wang L, et al. Radiomic features from multiparametric magnetic resonance imaging predict molecular subgroups of pediatric low-grade gliomas. BMC Cancer. 2023;23(1):848.","journal-title":"BMC Cancer"},{"issue":"12","key":"4511_CR19","doi-asserted-by":"publisher","first-page":"1009","DOI":"10.1159\/000540334","volume":"102","author":"M Erciyestepe","year":"2024","unstructured":"Erciyestepe M, Selvi O, Dinc G, et al. Factors affecting recurrence and survival in stage IIA colon cancer patients. Oncology. 2024;102(12):1009\u201317.","journal-title":"Oncology"},{"issue":"1","key":"4511_CR20","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/j.amjsurg.2020.10.032","volume":"222","author":"T Maruyama","year":"2021","unstructured":"Maruyama T, Shimoda M, Hakoda H, et al. Preoperative prognostic nutritional index predicts risk of recurrence after curative resection for stage IIA colon cancer. Am J Surg. 2021;222(1):179\u201385.","journal-title":"Am J Surg"},{"issue":"1","key":"4511_CR21","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1002\/ijc.34910","volume":"155","author":"N Horesh","year":"2024","unstructured":"Horesh N, Emile SH, Garoufalia Z, et al. Trends in management and outcomes of colon cancer in the united States over 15 years: analysis of the National cancer database. Int J Cancer. 2024;155(1):139\u201348.","journal-title":"Int J Cancer"},{"key":"4511_CR22","doi-asserted-by":"publisher","first-page":"1397468","DOI":"10.3389\/fonc.2024.1397468","volume":"14","author":"H Zeng","year":"2024","unstructured":"Zeng H, Zhong X, Liu W, et al. Predicting treatment failure in stage III colon cancer patients after radical surgery. Front Oncol. 2024;14:1397468.","journal-title":"Front Oncol"},{"key":"4511_CR23","doi-asserted-by":"crossref","unstructured":"Lee KH, Park SY, Song SH et al. Short-term outcomes of early versus conventional adjuvant chemotherapy in stage III colon cancer: randomized clinical trial. BJS Open. 2023;7(4).","DOI":"10.1093\/bjsopen\/zrad064"},{"issue":"5","key":"4511_CR24","doi-asserted-by":"publisher","first-page":"1869","DOI":"10.4251\/wjgo.v16.i5.1869","volume":"16","author":"B Liu","year":"2024","unstructured":"Liu B, Zhang ZX, Nie XY, et al. Clinical outcome and prognostic factors of T4N0M0 colon cancer after R0 resection: A retrospective study. World J Gastrointest Oncol. 2024;16(5):1869\u201377.","journal-title":"World J Gastrointest Oncol"},{"key":"4511_CR25","doi-asserted-by":"publisher","first-page":"758776","DOI":"10.3389\/fcell.2021.758776","volume":"9","author":"F Hu","year":"2021","unstructured":"Hu F, Wang J, Zhang M, et al. Comprehensive analysis of Subtype-Specific molecular characteristics of colon cancer: specific Genes, driver Genes, signaling Pathways, and immunotherapy responses. Front Cell Dev Biol. 2021;9:758776.","journal-title":"Front Cell Dev Biol"},{"issue":"2","key":"4511_CR26","doi-asserted-by":"publisher","first-page":"757","DOI":"10.21873\/anticanres.16867","volume":"44","author":"S Nakamura","year":"2024","unstructured":"Nakamura S, Kageyama H, Torii K, et al. High-risk features for recurrence in patients with stage III colorectal cancer: A retrospective cohort study. Anticancer Res. 2024;44(2):757\u201366.","journal-title":"Anticancer Res"},{"issue":"7","key":"4511_CR27","doi-asserted-by":"publisher","first-page":"1318","DOI":"10.1007\/s10147-020-01672-3","volume":"25","author":"H Matsuoka","year":"2020","unstructured":"Matsuoka H, Ando K, Hu Q, et al. Postoperative C-reactive protein\/albumin ratio is a biomarker of risk of recurrence and need for adjuvant chemotherapy for stage III colorectal cancer. Int J Clin Oncol. 2020;25(7):1318\u201326.","journal-title":"Int J Clin Oncol"},{"issue":"10","key":"4511_CR28","doi-asserted-by":"publisher","first-page":"958","DOI":"10.1007\/s00535-020-01706-7","volume":"55","author":"D Shida","year":"2020","unstructured":"Shida D, Inoue M, Tanabe T, et al. Prognostic impact of primary tumor location in stage III colorectal cancer-right-sided colon versus left-sided colon versus rectum: a nationwide multicenter retrospective study. J Gastroenterol. 2020;55(10):958\u201368.","journal-title":"J Gastroenterol"},{"issue":"20","key":"4511_CR29","doi-asserted-by":"publisher","first-page":"e70323","DOI":"10.1002\/cam4.70323","volume":"13","author":"J Qiu","year":"2024","unstructured":"Qiu J, Yu Y, Wang Z, et al. Developing individualized Follow-Up strategies based on High-Risk recurrence factors and dynamic risk assessment for locally advanced rectal cancer. Cancer Med. 2024;13(20):e70323.","journal-title":"Cancer Med"},{"issue":"5","key":"4511_CR30","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1007\/s00432-025-06221-x","volume":"151","author":"L Yan","year":"2025","unstructured":"Yan L, Ye Q, Shi B, et al. Random forest-based model for the recurrence prediction of borderline ovarian tumor: clinical development and validation. J Cancer Res Clin Oncol. 2025;151(5):160.","journal-title":"J Cancer Res Clin Oncol"},{"key":"4511_CR31","doi-asserted-by":"publisher","first-page":"1409755","DOI":"10.3389\/fgene.2024.1409755","volume":"15","author":"K Mendapara","year":"2024","unstructured":"Mendapara K. Development and evaluation of a chronic kidney disease risk prediction model using random forest. Front Genet. 2024;15:1409755.","journal-title":"Front Genet"},{"issue":"7","key":"4511_CR32","doi-asserted-by":"publisher","first-page":"4617","DOI":"10.21037\/qims-24-7","volume":"14","author":"Y Wang","year":"2024","unstructured":"Wang Y, Zhang L, Jiang Y, et al. Multiparametric magnetic resonance imaging (MRI)-based radiomics model explained by the Shapley additive explanations (SHAP) method for predicting complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicenter retrospective study. Quant Imaging Med Surg. 2024;14(7):4617\u201334.","journal-title":"Quant Imaging Med Surg"},{"issue":"1","key":"4511_CR33","doi-asserted-by":"publisher","first-page":"1226","DOI":"10.1186\/s12885-024-12951-x","volume":"24","author":"T Guo","year":"2024","unstructured":"Guo T, Cheng B, Li Y, et al. A radiomics model for predicting perineural invasion in stage II-III colon cancer based on computer tomography. BMC Cancer. 2024;24(1):1226.","journal-title":"BMC Cancer"},{"issue":"12","key":"4511_CR34","doi-asserted-by":"publisher","first-page":"110482","DOI":"10.1016\/j.ejso.2025.110482","volume":"51","author":"Y Zhou","year":"2025","unstructured":"Zhou Y, Zhao J, Tan Y, et al. CT-based radiomics deep learning signatures for noninvasive prediction of early recurrence after radical surgery in locally advanced colorectal cancer: A multicenter study. Eur J Surg Oncol. 2025;51(12):110482.","journal-title":"Eur J Surg Oncol"},{"issue":"1","key":"4511_CR35","doi-asserted-by":"publisher","first-page":"7561","DOI":"10.1038\/s41467-025-62910-8","volume":"16","author":"CML Loeffler","year":"2025","unstructured":"Loeffler CML, Bando H, Sainath S, et al. HIBRID: histology-based risk-stratification with deep learning and ctdna in colorectal cancer. Nat Commun. 2025;16(1):7561.","journal-title":"Nat Commun"},{"issue":"3","key":"4511_CR36","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1148\/radiol.2021203281","volume":"301","author":"Q Sun","year":"2021","unstructured":"Sun Q, Chen Y, Liang C, et al. Biologic pathways underlying prognostic radiomics phenotypes from paired MRI and RNA sequencing in glioblastoma. Radiology. 2021;301(3):654\u201363.","journal-title":"Radiology"},{"issue":"7","key":"4511_CR37","doi-asserted-by":"publisher","first-page":"5032","DOI":"10.1007\/s00330-020-07590-2","volume":"31","author":"Y Zhao","year":"2021","unstructured":"Zhao Y, Liu G, Sun Q, et al. Validation of CT radiomics for prediction of distant metastasis after surgical resection in patients with clear cell renal cell carcinoma: exploring the underlying signaling pathways. Eur Radiol. 2021;31(7):5032\u201340.","journal-title":"Eur Radiol"},{"issue":"1","key":"4511_CR38","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1186\/s41512-024-00177-1","volume":"8","author":"L Barrenada","year":"2024","unstructured":"Barrenada L, Dhiman P, Timmerman D, et al. Understanding overfitting in random forest for probability estimation: a visualization and simulation study. Diagn Progn Res. 2024;8(1):14.","journal-title":"Diagn Progn Res"}],"container-title":["BMC Gastroenterology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12876-025-04511-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12876-025-04511-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12876-025-04511-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T13:55:54Z","timestamp":1769694954000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s12876-025-04511-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,29]]},"references-count":38,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["4511"],"URL":"https:\/\/doi.org\/10.1186\/s12876-025-04511-9","relation":{},"ISSN":["1471-230X"],"issn-type":[{"value":"1471-230X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,29]]},"assertion":[{"value":"10 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study was approved by the Research Ethics Committees of Zhejiang Cancer Hospital (approval number IRB202-2025-340) and Dongyang Hospital affiliated with Wenzhou Medical University (approval number 2024-YX-281). As this was a retrospective, noninterventional study, the Institutional Review Board waived the requirement for written informed consent from patients. All experimental procedures involving human subjects were conducted in accordance with the ethical standards of the Institutional Review Board and the 1964 Declaration of Helsinki.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"72"}}