7.3 Injury setting as a moderator of post-concussive symptom trajectories following pediatric concussion
Bibliographic record
Abstract
Objective To determine whether differences in post-concussive symptoms (PCS) between children with concussion or orthopedic injuries (OI) are moderated by injury setting (i.e., sport-related versus non-sport-related). Design/Setting/Participants Prospective cohort study including children ages 8–16 with concussion (n=529; sport-related=81.9%) or OI (n=264; sport-related=82.3%) recruited in Emergency Departments at five hospitals across Canada. Assessment PCS were measured using the Health and Behaviour Inventory, a reliable and validated rating scale, at 2 weeks, 3 months, and 6 months post-injury. Outcomes Linear mixed model analyses examined group (concussion vs. OI), injury setting, and time post-injury as predictors of cognitive and somatic PCS. Main Results Mixed models revealed a significant group by setting by time interaction for cognitive (p =.032), but not somatic symptoms (p =.710). Cognitive symptoms were significantly worse in the concussion group compared to the OI group at 2 weeks post-injury for both sport-related (p < .001, d =.090) and non-sport-related settings (p < .001, d =.495), and for non-sport-related settings at both 3 (p =.020, d =.188) and 6 months (p =.004, d =.236) post-injury, but not for sport-related settings at 3 (p =.138, d =.106) or 6 months (p =.824, d =.018) post-injury. Group differences in cognitive PCS declined over time for both settings, but less rapidly for non-sport-related settings. Conclusions Cognitive PCS appear to be more pronounced and longer lasting when concussions are sustained in non-sport-related settings compared to sport-related settings. This moderating effect was not seen for somatic PCS, suggesting that trajectories of somatic and cognitive PCS are related to different predictors.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".