Determining Outcome in Children and Adolescents After Concussion: Viewing Things More Holistically
Bibliographic record
Abstract
Synopsis Pediatric and adolescent concussion is an increasingly high-profile public health issue, but it is also a highly heterogeneous phenomenon. Many factors interact dynamically to influence the recovery trajectory of adolescents and children. Diagnostic assessment must include domains other than self-reported symptoms, yet many prognostic models of outcome focus solely on the presence or absence of postconcussion symptoms to determine recovery. Function after concussion (recovery or persistence of problems) is the result of an interaction between biological, psychological, and social factors. Despite biopsychosocial models of assessment being advocated in rehabilitation for the last 20 years, they are still not routinely implemented in the evaluation of concussions, along the recovery trajectory, in children and adolescents. The International Classification of Functioning, Disability and Health is a framework anchored in a biopsychosocial perspective that can guide clinicians and researchers to include multiple perspectives in their assessments or research designs. By focusing on the patient as a person, researchers and clinicians can provide a more holistic approach that has the potential to contribute to a more successful and sustainable pediatric and adolescent concussion care model. J Orthop Sports Phys Ther 2019;49(11):855–863. Epub 9 Oct 2019. doi:10.2519/jospt.2019.8918
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".