Factors associated with follow-up care after pediatric concussion: A longitudinal population-based study in Alberta, Canada
Bibliographic record
Abstract
Background: Concussion is a common injury in children and adolescents. Current best practice guidelines indicate that recovery should be supervised through recurrent follow-up visits. A more detailed understanding of the system-level and individual factors that are associated with follow-up care is a critical step towards increasing evidence-based practice. The objective of this study was to identify predisposing, enabling, and need-based factors associated with follow-up care after pediatric concussion. Materials and methods: A retrospective population-based cohort study was conducted using linked, province-wide administrative health data for all patients <18 years of age with a diagnosis of concussion, other specified injuries of the head, unspecified injury of head, or post-concussion syndrome (PCS) between April 1, 2004 and March 31, 2018 in Alberta, Canada. The association between predisposing, enabling, and need-based factors and the receipt of follow-up care within a defined episode of care (EOC) was analyzed using logistic regression models for the entire cohort and for EOC that began with a concussion diagnosis. Predisposing factors included age and sex. Enabling factors included the community type of patient residence, area-based socioeconomic status (SES), and visit year. Need-based factors included where the EOC began (outpatient vs. emergency settings) and history of previous concussion-related EOC. Results: = 25,461). Males and adolescents were more likely to receive follow-up care. Follow-up was less likely among patients who lived in remote communities or in areas of lower SES, while EOCs beginning in 2011 or later were more likely to involve follow-up care. Patients whose EOC began in outpatient settings, had more than one EOC, or a diagnosis of concussion were more likely to receive follow-up care. Conclusion: Follow-up care for pediatric concussion has increased over time and is associated with patient age and sex, history of concussion-related EOC, where a patient lives (community type and area-based SES), and when and where the index visit occurs. A better understanding of which children are more likely to receive follow-up care, as well as how and when they do, is an important step in aligning practice with follow-up guidelines.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| 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".