Knowledge of paediatric concussion among front-line primary care providers
Bibliographic record
Abstract
OBJECTIVE: To assess the knowledge of paediatric concussion diagnosis and management among front-line primary care providers. METHODS: Experts from the Concussions Ontario Diagnosis and Early Education Working Group developed a 34-item survey incorporating case vignettes with the collaboration of experts in medical education. Electronic surveys were distributed via FluidSurveys using a modified version of Dillman's tailored design method. The survey was distributed to five Ontario professional associations. The target participants were front-line health care providers (family physicians, emergency medicine physicians, general paediatricians, nurse practitioners and physician assistants) in Ontario; only providers who diagnose and/or manage paediatric concussions were eligible to participate. RESULTS: The survey was fully completed by 577 health care providers who treat paediatric concussion. Of the respondents, 78% (95% CI 74% to 81%) reported diagnosing ≥5 concussions annually. Physicians and nonphysicians equally recognized concussion (90% [95% CI 86% to 92%]; 85% [95% CI 77% to 90%], respectively). Only 37% (95% CI 32% to 41%) of physicians correctly applied graduated return to play guidelines. Return to learn recommendations were also insufficient: 53% (95% CI 49% to 58%) neglected to recommend school absence and 40% (95% CI (35% to 44%) did not recommend schoolwork accommodations. Only 26% (95% CI 22% to 30%) of physicians reported regular use of concussion scoring scales. CONCLUSIONS: Considerable gaps in knowledge exist in front-line primary care providers with inadequate application of graduated return to play and return to learn following concussion, as demonstrated by the present broad population-based survey. Consistent application of best evidence-based management using comprehensive guidelines may help to reduce the impact of concussion and persistent postconcussive problems in children and adolescents.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".