Emotional response to sport concussion compared to ACL injury
Bibliographic record
Abstract
PRIMARY OBJECTIVES: To ascertain and compare the nature of emotional response of athletes to concussion and to anterior cruciate ligament (ACL) injury. RESEARCH DESIGN: Pre-injury, post-injury and longitudinal emotional functioning of athletes with concussion (n = 16), athletes with ACL injuries (n = 7) and uninjured athletes (n = 28) were compared in a prospective repeated-measures design. METHODS AND PROCEDURES: Participants completed the short version of the Profile of Mood States (POMS). ANOVAs and trend analysis were used to examine between and within group differences across time on two sub-scales, Total Mood Disturbance and Depression. MAIN OUTCOMES AND RESULTS: Athletes with ACL injury reported higher levels of depression for a longer duration than athletes with concussion. Relative to un-injured controls, athletes with concussion reported significant changes in Total Mood Disturbance and Depression post-injury, whereas athletes with ACL injuries reported significant changes in Depression scores only. Different patterns of post-injury emotional disturbance for the injured groups were observed by trend analyses. CONCLUSIONS: Concussed athletes do not report as much emotional disturbance as athletes with ACL injuries. Differential patterns of emotional disturbance were detected between injured groups. The authors recommended that clinical protocols and educational programmes address emotional sequelae associated with sport concussion and ACL injury.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".