5.7 Service delivery models for the management of pediatric and adolescent concussion: a systematic review
Bibliographic record
Abstract
Objective This systematic review examines the current peer-reviewed literature on pediatric concussion service delivery models (SDM) and relevant cost analyses. Design Systematic review; PubMed, Embase (Elsevier), CINAHL Plus (EBSCO), APA PsycINFO (EBSCO), Web of Science Core Collection, limited to human trials published in English from January 1, 2001 to January 10, 2022. Inclusion (i) Peer-reviewed (ii) evidence-based (iii) service delivery and/or associated health care costs (iv) mTBI, concussion, post-concussion symptoms of children up to age 18. Exclusion: Emergency Department-based interventions, adults, moderate or severe brain injuries. 1668 abstracts were independently screened by two reviewers followed by full-text screening of potentially included articles. A third blinded reviewer resolved inclusion/exclusion conflicts. This resulted in the inclusion of 28 articles. Outcome Measures To detect patterns of concussion care delivery; develop a system of what we called concussion ‘service delivery models’ (SDM); analyze the benefits, challenges, and costs of each SDM. Main Results The 28 articles were grouped into one of 3 categories: Generalist-Based Services (7), Specialist-Based Services (12) Web/Telemedicine Services (6). Four studies discussed costs relevant to SDMs. Conclusions There is a dearth of literature analyzing and comparing pediatric concussion SDMs. Cost analysis data are sparse and not generalizable. Clinicians and researchers need to develop a common language and criteria for the evaluation of effective concussion care delivery. This review proposes a simple SDM system as a starting point.
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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 | 0.029 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.012 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".