No cumulative effects for one or two previous concussions: Table 1
Bibliographic record
Abstract
BACKGROUND: Sports medicine clinicians and the general public are interested in the possible cumulative effects of concussion. OBJECTIVE: To examine whether athletes with a history of one or two previous concussions differed in their preseason neuropsychological test performances or symptom reporting. METHOD: Participants were 867 male high school and university amateur athletes who completed preseason testing with ImPACT version 2.0. They were sorted into three groups on the basis of number of previous concussions. There were 664 athletes with no previous concussions, 149 with one previous concussion, and 54 with two previous concussions. Multivariate analysis of variance was conducted using the verbal memory, visual memory, reaction time, processing speed, and postconcussion symptom composite scores as dependent variables and group membership as the independent variable. RESULTS: There was no significant multivariate effect, nor were there any significant main effects for individual scores. There was no measurable effect of one or two previous concussions on athletes' preseason neuropsychological test performance or symptom reporting. CONCLUSION: If there is a cumulative effect of one or two previous concussions, it is very small and undetectable using this methodology.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 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".