Tracking neuropsychological recovery following concussion in sport
Bibliographic record
Abstract
PRIMARY OBJECTIVE: The purpose of this study was to illustrate the serial use of computerized neuropsychological screening with ImPACT to monitor recovery in a clinical case series of injured athletes. METHODS AND PROCEDURES: Amateur athletes with concussions (n= 30, average age= 16.1, SD= 2.1 years) underwent pre-season testing and three post-concussion evaluations within the following intervals: 1-2 days, 3-7 days (M= 5.2 days) and 1-3 weeks (M= 10.3 days). The study selection criteria increased the probability of including athletes with slow recovery. RESULTS: Repeated measures ANOVAs revealed significant main effects for all five composite scores (verbal memory, visual memory, reaction time, processing speed and total symptoms). In group analyses, performance decrements and symptoms relating to concussion appeared to largely resolve by 5 days post-injury and fully resolve by 10 days. Athletes' scores were examined individually using the reliable change methodology. At 1 day post-injury, 90% had two or more reliable declines in performance or increases in symptom reporting. At 10 days, 37% were still showing two or more reliable changes from pre-season levels. CONCLUSIONS: This study illustrates the importance of analysing individual athletes' test data because group analyses can obscure slow recovery in a substantial minority of athletes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".