Service Delivery Models for the Management of Pediatric and Adolescent Concussion: A Systematic Review
Bibliographic record
Abstract
Objective: To examine the current peer-reviewed literature on pediatric concussion and mild traumatic brain injury (mTBI) service delivery models (SDMs) and relevant cost analyses. Data Sources: PubMed, Embase (Elsevier), CINAHL Plus (EBSCO), APA PsycINFO (EBSCO), and Web of Science Core Collection, limited to human trials published in English from January 1, 2001, to January 10, 2022. Study Selection: Included articles that (1) were peer-reviewed; (2) were evidence-based; (3) described service delivery and/or associated health care costs; and (4) focused on mTBI, concussion, or postconcussion symptoms of children and adolescents. Studies describing emergency department-based interventions, adults, and moderate to severe brain injuries were excluded. Data Extraction: The initial search resulted in 1668 articles. Using Rayyan software, 2 reviewers independently completed title and abstract screening followed by a full-text screening of potentially included articles. A third blinded reviewer resolved inclusion/exclusion conflicts among the other reviewers. This resulted in 28 articles included. Data Synthesis: Each of the 28 articles were grouped into 1 of the following 3 categories: generalist-based services (7), specialist-based services (12), and web/telemedicine services (6). One article discussed both generalists and specialists. It was clear that specialists are more proactive in their treatment of concussion than generalists. Most of the research on generalists emphasized the need for education and training. Four studies discussed costs relevant to SDMs. Conclusions: This review highlights the need for more discussion and formalized evaluation of SDMs to better understand concussion management. Overall there is more literature on specialist-based services than generalist-based services. Specialists and generalists have overarching similarities but differ often in their approach to pediatric concussion management. Cost analysis data are sparse and more research is needed.
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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 | 0.026 | 0.099 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".