{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T20:22:15Z","timestamp":1792182135054,"version":"4.4.0"},"reference-count":33,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T00:00:00Z","timestamp":1777852800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurol."],"abstract":"<jats:p>The transition to active rehabilitation in concussion care requires precise tools to identify patients at risk of persistent post-concussive symptoms (PPCS). While machine learning (ML) offers the potential to personalize prognosis, current models relying on clinical history and subjective symptom reporting (e.g., SCAT5) have failed to demonstrate significant performance gains over the last decade. This perspective article argues that clinical prognostic models have reached a performance ceiling of approximately 0.85 Area Under the Curve (AUC). By reviewing key studies from 2016 to 2025, we demonstrate that increasing algorithmic complexity\u2014from logistic regression to deep learning\u2014yields diminishing returns when applied to subjective inputs. In contrast, models incorporating physiological data, such as neuroimaging or fluid biomarkers, consistently break this ceiling, achieving AUCs exceeding 0.95. We conclude that better mathematics cannot correct for missing biological signal, and that the advancement of precision medicine in neurotrauma requires a fundamental shift toward multimodal, biological data integration.<\/jats:p>","DOI":"10.3389\/fneur.2026.1801044","type":"journal-article","created":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T04:24:43Z","timestamp":1777868683000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["The performance ceiling: why clinical data is insufficient for precision prognosis in concussion"],"prefix":"10.3389","volume":"17","author":[{"given":"Zach","family":"Napora","sequence":"first","affiliation":[{"name":"Sport Concussion Research and Service Laboratory, Department of Kinesiology, The Pennsylvania State University","place":["University Park, PA, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Owen","family":"Griffith","sequence":"additional","affiliation":[{"name":"Sport Concussion Research and Service Laboratory, Department of Kinesiology, The Pennsylvania State University","place":["University Park, PA, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Semyon","family":"Slobounov","sequence":"additional","affiliation":[{"name":"Sport Concussion Research and Service Laboratory, Department of Kinesiology, The Pennsylvania State University","place":["University Park, PA, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,5,4]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"695","DOI":"10.1136\/bjsports-2023-106898","article-title":"Consensus statement on concussion in sport: the 6th international conference on concussion in sport-Amsterdam, October 2022","volume":"57","author":"Patricios","year":"2023","journal-title":"Br J Sports Med"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"2","DOI":"10.3389\/fneur.2018.01113","article-title":"A multidimensional approach to post-concussion symptoms in mild traumatic brain injury","volume":"9","author":"Polinder","year":"2018","journal-title":"Front Neurol"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1007\/s11916-018-0724-2","article-title":"Factors associated with sport-related post-concussion headache and opportunities for treatment","volume":"22","author":"Register-Mihalik","year":"2018","journal-title":"Curr Pain Headache Rep"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"e16","DOI":"10.3399\/bjgp16X683161","article-title":"Persistent problems 1 year after mild traumatic brain injury: a longitudinal population study in New Zealand","volume":"66","author":"Theadom","year":"2016","journal-title":"Br J gen Pract J R Coll Gen Pract"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"941","DOI":"10.1136\/bjsports-2017-097729","article-title":"Predictors of clinical recovery from concussion: a systematic review","volume":"51","author":"Iverson","year":"2017","journal-title":"Br J Sports Med"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1016\/j.jsams.2024.11.001","article-title":"Early vestibular rehabilitation initiation is associated with faster recovery after sport related concussion","volume":"28","author":"Anderson","year":"2025","journal-title":"J Sci Med Sport"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"1272374","DOI":"10.3389\/fneur.2023.1272374","article-title":"Selective head cooling in the acute phase of concussive injury: a neuroimaging study","volume":"14","author":"Walter","year":"2023","journal-title":"Front Neurol"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"838","DOI":"10.1136\/bjsports-2017-097699","article-title":"Consensus statement on concussion in sport\u2014the 5th international conference on concussion in sport held in Berlin, October 2016","volume":"51","author":"McCrory","year":"2017","journal-title":"Br J Sports Med"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"e1003652","DOI":"10.1371\/journal.pmed.1003652","article-title":"Prediction of risk of prolonged post-concussion symptoms: derivation and validation of the TRICORDRR (Toronto Rehabilitation Institute concussion outcome determination and rehab recommendations) score","volume":"18","author":"Langer","year":"2021","journal-title":"PLoS Med"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"103646","DOI":"10.1016\/j.nicl.2024.103646","article-title":"Diffusion tensor analysis of white matter tracts is prognostic of persisting post-concussion symptoms in collegiate athletes","volume":"43","author":"Bert\u00f2","year":"2024","journal-title":"NeuroImage Clin"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"101626","DOI":"10.1016\/j.rehab.2021.101626","article-title":"Machine learning to predict sports-related concussion recovery using clinical data","volume":"65","author":"Chu","year":"2022","journal-title":"Ann Phys Rehabil Med"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"1362","DOI":"10.1249\/MSS.0000000000001903","article-title":"Machine learning in modeling high school sport concussion symptom resolve","volume":"51","author":"Bergeron","year":"2019","journal-title":"Med Sci Sports Exerc"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"408","DOI":"10.1097\/HTR.0000000000001116","article-title":"Patterns of concussion recovery and persisting symptoms in adolescent females","volume":"40","author":"Bunt","year":"2025","journal-title":"J Head Trauma Rehabil"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"979","DOI":"10.1080\/13854046.2025.2547933","article-title":"Using machine learning to predict concussion recovery time: the importance of psychological and symptomatic factors","volume":"140","author":"Taylor","year":"2025","journal-title":"Clin Neuropsychol"},{"key":"ref15","doi-asserted-by":"publisher","first-page":"1349","DOI":"10.1089\/neu.2022.0026","article-title":"Post-concussion symptoms rule: derivation and validation of a clinical decision rule for early prediction of persistent symptoms after a mild traumatic brain injury","volume":"39","author":"Le Sage","year":"2022","journal-title":"J Neurotrauma"},{"key":"ref16","doi-asserted-by":"publisher","first-page":"4349","DOI":"10.1007\/s00415-021-10566-x","article-title":"Saliva RNA biomarkers predict concussion duration and detect symptom recovery: a comparison with balance and cognitive testing","volume":"268","author":"Fedorchak","year":"2021","journal-title":"J Neurol"},{"key":"ref17","doi-asserted-by":"publisher","first-page":"751539","DOI":"10.3389\/fneur.2021.751539","article-title":"Self-reported complaints as prognostic markers for outcome after mild traumatic brain injury in elderly: a machine learning approach","volume":"12","author":"Bittencourt","year":"2021","journal-title":"Front Neurol"},{"key":"ref18","doi-asserted-by":"publisher","first-page":"830","DOI":"10.1089\/neu.2020.7018","article-title":"Predicting post-concussion symptom recovery in adolescents using a novel artificial intelligence","volume":"38","author":"Fleck","year":"2021","journal-title":"J Neurotrauma"},{"key":"ref19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5812\/ijpbs.82799","article-title":"Forecasting psychological symptoms in mild traumatic brain injury: a nonparametric approach","volume":"13","author":"Nademi","year":"2018","journal-title":"Iranian J Psychiatry Behav Sci"},{"key":"ref20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.17795\/ijpbs-5849","article-title":"Comparison of artificial neural network and logistic regression models for prediction of psychological symptom six months after mild traumatic brain injury","volume":"11","author":"Shafiei","year":"2016","journal-title":"Iranian J Psychiatry Behav Sci"},{"key":"ref21","doi-asserted-by":"publisher","first-page":"1533","DOI":"10.1093\/arclin\/acaf066","article-title":"Get your brain in the game: using machine learning to predict recovery timelines following sports-related concussion","volume":"40","author":"Thomas","year":"2025","journal-title":"Arch Clin Neuropsychol Off J Natl Acad Neuropsychol"},{"key":"ref22","doi-asserted-by":"publisher","first-page":"422","DOI":"10.18103\/mra.v13i1.6193","article-title":"Comparison of machine learning models in predicting mental health sequelae following concussion in youth","volume":"2025","author":"Peng","year":"2025","journal-title":"AMIA Summits Transl Sci Proc"},{"key":"ref23","doi-asserted-by":"publisher","first-page":"1112","DOI":"10.1089\/neu.2022.0220","article-title":"Assessing pediatric mild traumatic brain injury and its recovery using resting-state magnetoencephalography source magnitude imaging and machine learning","volume":"40","author":"Huang","year":"2023","journal-title":"J Neurotrauma"},{"key":"ref24","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.compbiomed.2018.09.011","article-title":"A multimodal biomarker for concussion identification, prognosis and management","volume":"102","author":"Jacquin","year":"2018","journal-title":"Comput Biol Med"},{"key":"ref25","doi-asserted-by":"publisher","first-page":"e002090","DOI":"10.1136\/bmjsem-2024-002090","article-title":"Developing a multivariate model for the prediction of concussion recovery in sportspeople: a machine learning approach","volume":"11","author":"Yates","year":"2025","journal-title":"BMJ Open Sport Exerc Med"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.3390\/jpm12020196","article-title":"Personalized prediction of postconcussive working memory decline: a feasibility study","volume":"12","author":"Chen","year":"2022","journal-title":"J Pers Med"},{"key":"ref27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18103\/mra.v13i1.6193","article-title":"Network-dependent variability in functional connectivity after mild traumatic brain injury and repetitive head impacts: a systematic review","volume":"13","author":"Griffith","year":"2025","journal-title":"Med Res Arch"},{"key":"ref28","doi-asserted-by":"publisher","first-page":"125","DOI":"10.3389\/fneur.2017.00125","article-title":"Predicting outcome 12 months after mild traumatic brain injury in patients admitted to a neurosurgery service","volume":"8","author":"Hellstr\u00f8m","year":"2017","journal-title":"Front Neurol"},{"key":"ref29","doi-asserted-by":"publisher","first-page":"1365","DOI":"10.1109\/TCBB.2021.3091972","article-title":"Heterogeneity in blood biomarker trajectories after mild TBI revealed by unsupervised learning","volume":"19","author":"Bui","year":"2022","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"ref30","doi-asserted-by":"publisher","first-page":"1124","DOI":"10.1111\/head.14955","article-title":"Identifying and predicting headache trajectories among those with acute post-traumatic headache","volume":"65","author":"Mao","year":"2025","journal-title":"Headache"},{"key":"ref31","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1109\/TNSRE.2008.918422","article-title":"Automatic classification of athletes with residual functional deficits following concussion by means of EEG signal using support vector machine","volume":"16","author":"Cao","year":"2008","journal-title":"IEEE Trans Neural Syst Rehabil Eng Publ IEEE Eng Med Biol Soc"},{"key":"ref32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fneur.2021.769819","article-title":"Evaluation of machine learning techniques to predict the likelihood of mental health conditions following a first mTBI","volume":"12","author":"Dabek","year":"2022","journal-title":"Front Neurol"},{"key":"ref33","doi-asserted-by":"publisher","first-page":"12686","DOI":"10.1038\/s41598-024-63540-8","article-title":"Classification of short and long term mild traumatic brain injury using computerized eye tracking","volume":"14","author":"Cade","year":"2024","journal-title":"Sci Rep"}],"container-title":["Frontiers in Neurology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fneur.2026.1801044\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T04:24:43Z","timestamp":1777868683000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fneur.2026.1801044\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,4]]},"references-count":33,"alternative-id":["10.3389\/fneur.2026.1801044"],"URL":"https:\/\/doi.org\/10.3389\/fneur.2026.1801044","relation":{},"ISSN":["1664-2295"],"issn-type":[{"value":"1664-2295","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,4]]},"article-number":"1801044"}}