3.17 Longitudinal changes in depressive symptoms among adolescents with acute sport-related concussion or fracture injury
Bibliographic record
Abstract
Objective Compare longitudinal changes in depressive symptoms among pediatric acute sport-related concussion (SRC) or sport-related fractures (SRF). Design Prospective cohort study. Setting Tertiary concussion clinic and fracture clinic. Participants Adolescents (14–18 years) diagnosed with an isolated acute SRC (n=74) or an isolated SRF (n=99) (65.9% male, mean age 15.0 [SD 1.0]). Interventions (or Assessment of Risk Factors) SRC or SRF. Outcome Measures Patient Health Questionnaire for Adolescents Depression Scale (PHQ-A-9) administered at initial assessment, subsequent follow-up visits, and clinical recovery. Mild or worse depressive symptoms was PHQ-A-9 score ≥4. Main Results There were no group differences in preinjury history of mental health or learning disorders or family history of mental health disorders. At initial assessment, median PHQ-A-9 score was significantly higher among adolescent SRC than SRF patients (SRC: 6; IQR: 3–9 vs SRF: 3; IQR: 1–6; p=0.0008). Significantly more SRC patients reported PHQ-A-9 score ≥4 at their initial appointment than SRF patients (62.2% vs 33.0%; p<0.0001). At recovery, there was no difference in PHQ-9-A scores (SRC: 0; IQR: 0–2 vs SRF: 1; IQR: 0–3; p=0.39); however, fewer SRC patients reported PHQ-A-9 score ≥4 than SRF patients (6.8% vs 17.7%; p=0.038). Controlling for initial PHQ-A-9 score ≥4, age, and sex, SRC patients had significantly lower odds of PHQ-A-9 score ≥4 at recovery (OR: 0.17; 95% CI: 0.05–0.56). Conclusions At recovery, a notable proportion of SRC and SRF patients report mild or worse depressive symptoms. Research should identify risk factors for developing depressive disorders.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".