What domains of clinical function should be assessed after sport-related concussion? A systematic review
Bibliographic record
Abstract
BACKGROUND: Sport-related concussion (SRC) is a clinical diagnosis made after a sport-related head trauma. Inconsistency exists regarding appropriate methods for assessing SRC, which focus largely on symptom-scores, neurocognitive functioning and postural stability. DESIGN: Systematic literature review. DATA SOURCES: MEDLINE, EMBASE, PsycINFO, Cochrane-DSR, Cochrane CRCT, CINAHL, SPORTDiscus (accessed July 9, 2016). ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Original (prospective) studies reporting on postinjury assessment in a clinical setting and evaluation of diagnostic tools within 2 weeks after an SRC. RESULTS: Forty-six studies covering 3284 athletes were included out of 2170 articles. Only the prospective studies were considered for final analysis (n=33; 2416 athletes). Concussion diagnosis was typically made on the sideline by an (certified) athletic trainer (55.0%), mainly on the basis of results from a symptom-based questionnaire. Clinical domains affected included cognitive, vestibular and headache/migraine. Headache, fatigue, difficulty concentrating and dizziness were the symptoms most frequently reported. Neurocognitive testing was used in 30/33 studies (90.9%), whereas balance was assessed in 9/33 studies (27.3%). SUMMARY/CONCLUSIONS: The overall quality of the studies was considered low. The absence of an objective, gold standard criterion makes the accurate diagnosis of SRC challenging. Current approaches tend to emphasise cognition, symptom assessment and postural stability with less of a focus on other domains of functioning. We propose that the clinical assessment of SRC should be symptom based and interdisciplinary. Whenever possible, the SRC assessment should incorporate neurological, vestibular, ocular motor, visual, neurocognitive, psychological and cervical aspects.
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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 | 0.015 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".