The Canadian Pediatric Mild Traumatic Brain Injury Common Data Elements Project: Harmonizing Outcomes to Increase Understanding of Pediatric Concussion
Bibliographic record
Abstract
A critical component for accelerating the clinical uptake of research data in the area of pediatric concussion or mild traumatic brain injury (MTBI) pertains to the establishment and utilization of common databases. The objective of the first phase of our CanPedCDE initiative was to agree upon pediatric common data elements (CDEs) that could best characterize children with MTBI over their recovery period. The selection of CDEs for our framework aimed to balance factors such as the comprehensiveness of outcomes collected, their applicability to diverse settings, as well as the costs associated with their use. Selection began by identifying relevant domains of functioning (e.g., post-concussion symptoms, attention, and balance). Two sources were used to make this process more efficient: 1) the World Health Organization International Classification of Functioning (ICF) Traumatic Brain Injury Core Set, and the U.S. National Institute of Neurological Disorders and Stroke Traumatic Brain Injury Common Data Elements, both of which had already suggested relevant domains to include in TBI research. The process was completed in two phases: 1) using an online survey of experts and 2) through an in-person consensus meeting. Measurement tools were also proposed that were best felt to capture these domains. Forty experts in MTBI in children from multiple health-related perspectives (e.g., emergency medicine, pediatrics, neurosurgery, nursing, physiotherapy, and neuroscience), as well as knowledge users, participated in the selection process. The final list of CDEs included 77 distinct areas of functioning, covering all categories of the ICF model. Outcome measures were attached to each element, when applicable. The CanPedCDE initiative addresses a significant limitation in MTBI research to date and may help both researchers and clinicians to organize and standardize their assessment of children and youth post-MTBI in order to move the field in promising directions.
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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 | 0.081 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.016 | 0.026 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".