Association Between Preinjury Symptoms and Postconcussion Symptoms at 4 Weeks in Youth
Bibliographic record
Abstract
OBJECTIVE: To investigate whether preinjury physical, emotional, cognitive, and sleep symptoms on the Post-Concussion Symptoms Inventory (PCSI) are associated with persistent postconcussion symptoms (PPCS) at 4 weeks and whether any associations are moderated by sex or age. STUDY SETTING AND PARTICIPANTS: A total of 3063 participants with acute concussion, presenting to 9 Canadian pediatric emergency departments, were enrolled from August 2013 to June 2015. DESIGN: A planned secondary analysis of a prospective, multicenter cohort study (Predicting Persistent Post-concussive Problems in Pediatrics or 5P). Primary outcome was PPCS at 4 weeks, defined as 3 or more new or worsening individual symptoms compared with the preinjury score at 28 days on the PCSI. The association between preinjury scores and PPCS was analyzed with a multivariable logistic regression analysis that included preinjury, sex, age, sex × preinjury, and age × preinjury interactions as predictors. Missing baseline covariates were imputed. RESULTS: A total of 2123 (n = 844 [39.8%] girls; median [IQR] age = 12.9 [10.7, 15.0] participants were included in the analysis. Preinjury physical symptom score was associated with PPCS at 4 weeks (χ2 = 13.87, df = 6, P = .031). The preinjury emotional score also contributed to the variability in PPCS (χ2 = 11.79, df = 6, P = .067). While girls reported higher preinjury physical, emotional, and cognitive scores than boys, neither sex nor age interacted with preinjury to predict PPCS at 4 weeks. Independent of age and sex, preinjury physical symptoms were associated with PPCS at 4 weeks (OR = 1.40; 95% CI, 1.15-1.70). CONCLUSION: Preinjury physical symptoms are associated with the probability of having PPCS at 4 weeks postconcussion independent of age and sex. Providers should consider preinjury symptoms to inform prognosis and recovery management.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".