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Record W2578371959 · doi:10.1097/sap.0000000000000959

Pediatric Hand Surgery in Global Health

2017· review· en· W2578371959 on OpenAlexaff
Karen Y. Chung, Amanda Hanemaayer, Dan Poenaru

Bibliographic record

VenueAnnals of Plastic Surgery · 2017
Typereview
Languageen
FieldMedicine
TopicBone fractures and treatments
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineMEDLINEOutreachGlobal healthFamily medicinePediatric surgerySystematic reviewMedical educationSurgeryPublic healthNursing

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.820
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.242
GPT teacher head0.454
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations10
Published2017
Admission routes1
Has abstractyes

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