Evidence-based approach to revising the SCAT2: introducing the SCAT3
Bibliographic record
Abstract
The Sport Concussion Assessment Tool 2 (SCAT2), which evolved from the 2008 Concussion in Sport Group (CISG) Consensus meeting, has been widely used internationally for the past 4 years. Although the instrument is considered very practical and moderately effective for use by clinicians who manage concussion, the utility and sensitivity of a 100-point scoring system for the SCAT2 has been questioned. The 2012 CISG Consensus Meeting provided an opportunity for several of the world's leading concussion researchers and clinicians to present data and to share experiences using the SCAT2. The purpose of this report is to consider recommendations by the CISG, and to review the current literature to identify the most sensitive and reliable concussion assessment components for inclusion in a revised version-the SCAT3. Through this process, it was determined that important clinical information can be ascertained in a streamlined manner through the use of a multimodal instrument such as the SCAT3. This test battery should include an initial assessment of injury severity using the Glasgow Coma Scale, immediately followed by observing and documenting concussion signs. Once this is complete, symptom endorsement and symptom severity, neurocognitive function and balance function should be assessed in any athlete suspected of sustaining a concussion. There is no evidence to support the use of a composite/total score; however, there is good evidence to support the use of each component (scored independently) in a revised assessment tool.
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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 | 0.115 | 0.221 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.017 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".