What tests and measures should be added to the SCAT3 and related tests to improve their reliability, sensitivity and/or specificity in sideline concussion diagnosis? A systematic review
Bibliographic record
Abstract
OBJECTIVES: Several iterations of the Sport Concussion Assessment Tool (SCAT) have been published over the past 16 years. Our goal was to systematically review the literature related to the SCAT and provide recommendations for improving the tool. To achieve this goal, five separate but related searches were conducted and presented herein. DESIGN: Systematic literature review. DATA SOURCES: Medline, Embase, PsycINFO, Cumulative Index of Nursing and Allied Health Literature (CINAHL), Cochrane Central Register of Controlled Trials, SPORTDiscus and PubMed. ELIGIBILITY CRITERIA: Original, empirical, peer-reviewed findings published in English and included sports-related concussion (SRC). Review papers, case studies, editorials and conference proceedings/abstracts were excluded. The age range for the ChildSCAT was 5-12 years and for the Adult SCAT was 13 years and above. RESULTS: Out of 2961 articles screened, a total of 96 articles were included across the five searches. Searches were not mutually exclusive. The final number of articles included in the qualitative synthesis for each search was 21 on Adult SCAT, 32 on ChildSCAT, 21 on sideline, 8 on video/observation and 14 on oculomotor. SUMMARY/CONCLUSIONS: The SCAT is the most widely accepted and deployable sport concussion assessment and screening tool currently available. There is some degree of support for using the SCAT2/SCAT3 and ChildSCAT3 in the evaluation of SRC, with and without baseline data. The addition of an oculomotor examination seems indicated, although the most valid method for assessing oculomotor function is not clear. Video-observable signs of concussion show promise, but there is insufficient evidence to warrant widespread use at this time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.051 | 0.186 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.017 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 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".