Day of injury assessment of sport-related concussion
Bibliographic record
Abstract
OBJECTIVE: To conduct a critical review of the literature on instruments currently used in the assessment of sport-related concussion on the day of injury. DATA SOURCES: Computerised searches of the literature posted to MEDLINE, PubMed, CINAHL, PsychInfo and Cochrane Library from 1 January 1982 through 21 August 2012. Key words and medical embedded subheadings (MeSH) terms relevant to sport-related concussion were applied, which identified 577 articles. STUDY SELECTION: In addition to MeSH term and key word criteria, a study was included in the analysis if the article: (1) was published in English, (2) represented original research, (3) pertained to sport-related concussion (ie, not non-sports traumatic brain injury), (4) included assessment or diagnostic data collected within 24 h of the injury event and (5) involved human research. A total of 41 studies qualified for review. DATA EXTRACTION: All articles were examined to determine if the study met the additional requirements for inclusion. A standardised method was used to document critical elements of the study design, population, tests employed and key findings. DATA SYNTHESIS: A large number of studies were analysed that reported data from testing conducted within 24 h of injury. These studies collectively demonstrated that a number of instruments are capable of measuring the acute effects of concussion across several domains, such as symptoms, cognition and balance. RESULTS: Relating to specific assessment domains are compiled in separate tables and an interpretive summary of the findings is provided. CONCLUSIONS: Several well-validated tests are appropriate for use in the assessment of acute concussion in the competitive sporting environment. These tests provide important data on the symptoms and functional impairments that clinicians can incorporate into their diagnostic formulation, but they should not solely be used to diagnose concussion.
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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 | 0.042 | 0.154 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.023 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".