Role of sport-related concussion on academic achievement among youth athletes
Bibliographic record
Abstract
Objective Determine change in report card grades in youth after sustaining an acute sport-related concussion. Design Prospective case-series. Setting Paediatric, multi-disciplinary concussion. Participants 48 youth athletes (mean age 14.3; SD: 0.9; male: 57.6%) who sustained a sport-related concussion during the school year. Overall, 3 youth were lost to follow-up and 12 youth submitted only one report card. Interventions (or assessment of risk factors) Subsequently developing Post-Concussion Syndrome (PCS: symptomatic after 30 days), initial concussion severity (Post Concussion Symptom Scale), self-reported concussion history, sex, age, and academic accommodations during recovery. Outcome measures The report card immediately before the concussion and the report card immediately after medical clearance were collected. Pre-concussion and post-concussion overall and core grade-point average was calculated from report cards. Core grade-point average (GPA) included math, sciences, social studies, English, and foreign languages. Main results Overall GPA was 82.9% (SD 8.5%) pre-concussion and 82.7% (SD 8.0%) after concussion recovery (difference: −0.2%, 95% CI: −1.6%, 1.1%). Core GPA was 80.0% (SD 10.1%) pre-concussion and 79.4% (SD 10.4%) after concussion recovery (difference: −0.6%, 95% CI: −2.8%, 1.6%). There were no significant differences in overall or core GPA when stratified by sex, age, initial concussion severity, previous concussion history, receiving adequate school accommodations during recovery, or subsequently developing PCS. Conclusions There were no statistically or clinically significant changes in overall or core GPA during concussion recovery. Further research is needed to determine if there are more subtle changes in academic performance during the recovery process. Competing interests None.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".