Early Postinjury Screen Time and Concussion Recovery
Bibliographic record
Abstract
OBJECTIVES: To determine the association between early screen time (7-10 days postinjury) and postconcussion symptom severity in children and adolescents with concussion, as compared to those with orthopedic injury (OI). METHODS: This was a planned secondary analysis of a prospective longitudinal cohort study. Participants were 633 children and adolescents with acute concussion and 334 with OI aged 8 to 16, recruited from 5 Canadian pediatric emergency departments. Postconcussion symptoms were measured using the Health and Behavior Inventory at 7 to 10 days, weekly for 3 months, and biweekly from 3 to 6 months postinjury. Screen time was measured by using the Healthy Lifestyle Behavior Questionnaire. Generalized least squares models were fit for 4 Health and Behavior Inventory outcomes (self- and parent-reported cognitive and somatic symptoms), with predictors including screen time, covariates associated with concussion recovery, and 2 3-way interactions (self- and parent-reported screen time with group and time postinjury). RESULTS: Screen time was a significant but nonlinear moderator of group differences in postconcussion symptom severity for parent-reported somatic (P = .01) and self-reported cognitive symptoms (P = .03). Low and high screen time were both associated with relatively more severe symptoms in the concussion group compared to the OI group during the first 30 days postinjury but not after 30 days. Other risk factors and health behaviors had stronger associations with symptom severity than screen time. CONCLUSIONS: The association of early screen time with postconcussion symptoms is not linear. Recommending moderation in screen time may be the best approach to clinical 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 | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 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".