Concussion Classification in a Multicenter Patient Cohort: The Updated ACRM Diagnostic Criteria and Concordance With Physician Impression of Injury
Bibliographic record
Abstract
OBJECTIVE: The American Congress of Rehabilitation Medicine (ACRM) substantially revised its diagnostic criteria for mild traumatic brain injury (mTBI) in 2023, encompassing acute symptoms and positive clinical and laboratory examinations, in addition to immediate signs of injury. This study aimed to apply these criteria to a large, diverse cohort and compare the diagnostic determination to physician impression of injury. SETTING: A network of 3 concussion specialty clinics in Ontario, Canada. PARTICIPANTS: A total of 1447 patients (61.0% female; median age = 26 years [IQR: 15-42 years, range: 3-87 years]; days post-injury (median: 23 IQR: [13-47, 0-349]) completed initial evaluations between June 28, 2024 and June 18, 2025. DESIGN: Prospective observational study. MAIN MEASURES: Occurrence rates were calculated, and binary/ordinal logistic regressions were applied to determine if individual criterion endorsement or diagnostic outcome ("Definite," "Suspected," and "No mTBI") was associated with age, sex, symptoms at clinical presentation, or days post-injury. Additionally, concordance with physician impression was assessed similarly. RESULTS: Criteria for signs, symptoms, and clinical examinations were all more likely to be endorsed with increasing symptom severity at presentation, as was an ACRM diagnostic outcome of definite mTBI. Shorter time post-injury was associated with positive clinical/laboratory examinations and the presence of confounding factors, in addition to a definite diagnostic outcome. A total of 18.4% of cases were classified less definitively as mTBI by physician impression than by the updated ACRM diagnosis, with physicians tending toward underdiagnosis, particularly in patients reporting lower current symptom severity. CONCLUSION: Use of the ACRM criteria clinically to determine if an injury qualifies as mTBI may be less susceptible to bias from ongoing symptom reporting than the physician impression of the injury.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".