Evaluating Adherence to Return to School and Activity Protocols in Children After Concussion
Bibliographic record
Abstract
OBJECTIVE: The need to have a pediatric-specific concussion management protocol on Return to School (RTS) and Return to Activity (RTA) after concussion has been recognized internationally. The first step to evaluate the protocol effectiveness is to establish whether children and youth are adhering to these recommendations. The objective of this study was to explore the prevalence and predictors of adherence to RTS and RTA concussion management protocols for children/youth. DESIGN: A prospective cohort of children/youth with concussion. SETTING: Childhood Disability Research Centre. PARTICIPANTS: One hundred thirty-nine children/youth aged 5 to 18 years, diagnosed with concussion and symptomatic upon enrollment, were followed for up to 6 months. Primary recruitment occurred from a Children's Hospital Emergency Department. INTERVENTION: Provision of RTS/RTA guidelines. MAIN OUTCOME MEASURES: Measurement of adherence came from multiple sources, including the child's and parent's knowledge of protocols, research personnel evaluations, and self-reported stages of RTS/RTA and Post-Concussion Symptom Scale (PCSS) scores. RESULTS: Spearman correlations and logistic regression were used, investigating the relationship between PCSS and progression of protocols and determining predictors of adherence. Significant negative associations between total PCSS score and stage of RTS/RTA protocols were found. Fifty-three percent and 56% of the participants adhered to the RTS and RTA protocols, respectively. CONCLUSIONS: Children's knowledge of protocols and total PCSS scores significantly predicted adherence to RTS/RTA and may be the most important factors in predicting adherence during recovery from concussion.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".