Factors Associated with Quality of Life in Adults with Persistent Post-Concussion Symptoms
Bibliographic record
Abstract
OBJECTIVE: To assess demographic, clinical, and injury characteristics associated with health-related quality of life (HRQOL) in adults with persistent post-concussion symptoms (PPCS). METHODS: Adults with PPCS presenting to a specialized brain injury clinic completed demographic, injury, and clinical outcome questionnaires at the initial clinic assessment. Clinical outcome measures were collected including the Rivermead Post-Concussion Symptoms Questionnaire (RPQ), Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder Scale-7 (GAD-7), and the Fatigue Severity Scale (FSS). HRQOL was measured using the Quality of Life after Brain Injury (QOLIBRI) questionnaire. Stepwise hierarchical multiple regression analysis adjusting for age, sex, and months since injury was used to determine associations between quality of life and clinical outcome measures. RESULTS: Overall, 125 participants were included. The PHQ-9, FSS, and GAD-7 were significant predictors of QOLIBRI scores (R2 = 0.481, p < .001), indicating that participants with higher levels of depressive symptoms, fatigue, and anxiety reported poorer HRQOL. The PHQ-9 score was the strongest predictor, accounting for 42.0% of the variance in QOLIBRI scores. No demographic or injury characteristics significantly predicted QOLIBRI scores. There was a high prevalence of depressive symptoms with 72.8% of participants having PHQ-9 scores ≥ 10. CONCLUSION: Among patients with PPCS, mental health and fatigue are important contributors to HRQOL. As there is a high burden of mood disorders and fatigue in this population, targeted treatments for these concerns may impact the quality of life.
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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 | 0.004 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".