Evolving the SCAT5 for Ruling Out Higher-Severity Traumatic Brain Injuries—Can Decision Rules Developed for Emergency Settings Help?
Bibliographic record
Abstract
BACKGROUND: Decision rules (eg, Canadian computed tomography head rule [CCHR] for adults and Pediatric Emergency Care Applied Research Network [PECARN] rule for children/adolescents) are used in emergency settings (emergency room [ER] rules) to assess traumatic brain injuries (TBIs). The rules have a high-sensitivity and near-perfect negative predictive value that help to rule out more severe TBI. CLINICAL QUESTION: Which criteria should be added to the Sport Concussion Assessment Tool 5 (SCAT5) to reach the sensitivity of the ER rules and improve the utility of the SCAT5 for screening for higher-severity head and brain injuries? KEY RESULTS: We performed a comparative analysis of the SCAT5 with the CCHR and PECARN rules. We compared the presence (yes or no) and comparative “face value” sensitivity (lower, identical, or higher) of the SCAT5 criteria to the ER rules criteria. Loss of consciousness, vomiting, severe/increasing headache, and seizure are SCAT “red flags” with similar or higher sensitivity compared to ER rules criteria. Five criteria had lower sensitivity or were absent from the SCAT. Emergency room rules include any abnormality on the Glasgow Coma Scale (GCS<15), but only a “deterioration of the state of consciousness” is considered a “red flag” in the SCAT5. Persistent retrograde amnesia for more than 30 minutes, age>65, severity of the mechanism of injury, and signs of skull fractures are not mentioned in the SCAT5. CLINICAL APPLICATION: We identified 5 criteria that could inform the evolution the SCAT5 to improve its ability to rule out more severe TBI in a sideline assessment context. J Orthop Sports Phys Ther 2023;53(3):113–119. Epub: 9 December 2022. doi:10.2519/jospt.2022.11301
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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 | 0.050 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".