Cumulative effects of concussion in amateur athletes
Bibliographic record
Abstract
PRIMARY OBJECTIVE: To examine the possibility that athletes with multiple concussions show cumulative effects of injury. METHODS AND PROCEDURES: Amateur athletes with a history of three or more concussions were carefully matched (gender, age, education and sport) with athletes with no prior concussions. All completed a computerized neuropsychological test battery at preseason (ImPACT) and then within 5 days of sustaining a concussion (mean = 1.7 days). MAIN OUTCOMES AND RESULTS: There were differences between groups in symptom reporting and memory performance. At baseline (i.e. preseason), athletes with multiple concussions reported more symptoms than athletes with no history of concussion. At approximately 2 days post-injury, athletes with multiple concussions scored significantly lower on memory testing than athletes with a single concussion. Athletes with multiple concussions were 7.7 times more likely to demonstrate a major drop in memory perfomance than athletes with no previous concussions. CONCLUSIONS: This study provides preliminary evidence to suggest that athletes with multiple concussions might have cumulative effects.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".