Pediatric Hand Surgery in Global Health
Bibliographic record
Abstract
PURPOSE: There is emerging interest in hand surgery and global health. This was emphasized at the 2015 presidential address at the American Society for Surgery of Hand. Children are prioritized because of their increased risk for trauma and higher potential for better outcomes. This study aims to identify how hand surgical volunteer programs can benefit the pediatric hand surgical landscape in global health. There has been no literature review to date. METHODS: This institutional review board-approved review systematically searched PubMed, Embase, Medline, African Journal Online, and the Journal of Hand Surgery. A scoping review methodology was selected to allow mapping of a body of literature by topic, include a greater range of study designs, and provide a descriptive overview of the reviewed material. All studies published between 2000 to March 2016 relevant to pediatric hand surgery in global health were included. Preferred Reporting Items for Systematic Reviews and Meta-Analyses was used to record the search results. RESULTS: Six hundred sixty-eight citations were reviewed, with 10 studies that satisfied the inclusion criteria. Hand trauma (70%), congenital anomalies (30%), tumors (20%), surgical technique (50%), and international outreach recommendation (30%) were common themes. Targeting prevention (50%), international outreach education (30%), and building on previous studies to validate findings study (10%) were identified as gaps.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".