6.13 The effect of pre-existing mental health conditions on symptom recovery outcomes in adolescents with sport-related concussion
Bibliographic record
Abstract
Objective To determine if pre-existing mental health conditions are associated with symptom outcomes following sport-related concussion (SRC) in adolescents. Design Secondary analysis of prospective, multisite cohort (5P Study). Setting Canadian pediatric emergency departments (PED, n=9). Participants Adolescents with SRC (n=968; age=15.1±1.3yrs; 57.6% male). Interventions (or Assessment of Risk Factors) Adolescents were categorized as pre-existing anxiety only (n=61), depression only (n=17), anxiety and depression (n=32), or control (no mental health diagnoses, n=858) based on self-reported medical history. Participants completed the Post-Concussion Symptom Inventory (PCSI) in the PED and at 1, 2, 4, 8, and 12-weeks post-injury. Outcome Measures PCSI change scores (total post-injury minus pre-injury symptoms) were calculated, with higher scores indicating more frequent/severe symptoms post-injury. Persistent Post-Concussion Symptoms (PPCS) were defined as three new/worse symptoms relative to pre-injury scores at the 4-week assessment. Main Results Pre-existing depression and combined anxiety/depression groups had significantly higher symptom scores across the study period than controls in univariate analyses (p=0.003); however, symptom resolution over time did not differ by group (i.e. no interaction effect, p=0.50). Youth with combined anxiety/depression (n=18, 64.3%) had higher odds of experiencing PPCS than controls (n=337; Odds Ratio= 2.64, 95% CI= 1.20, 5.81; p=0.016). In multivariate analyses, mental health status was not related to symptom recovery (group effect: p=0.32, interaction effect= p=0.46) or PPCS risk (p=0.62). Conclusions Combined anxiety/depression diagnoses were associated with worse symptom outcomes in univariate models, but this association was not present when controlling for known covariates of symptom recovery. Future studies should evaluate mental health status on other clinical outcomes.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".