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Record W2608718411 · doi:10.1097/gox.0000000000001273

Plastic and Reconstructive Surgery in Global Health: Let’s Reconstruct Global Surgery

2017· article· en· W2608718411 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePlastic & Reconstructive Surgery Global Open · 2017
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOutreachReconstructive surgeryGlobal healthMedicinePlastic surgeryCommissionPrioritizationValue (mathematics)Public relationsSurgeryPolitical scienceEngineeringComputer scienceNursingPublic healthManagement scienceLaw

Abstract

fetched live from OpenAlex

Since the inception of the Lancet Commission in 2013 and consequent prioritization of Global Surgery at the World Health Assembly, international surgical outreach efforts have increased and become more synergistic. Plastic surgeons have been involved in international outreach for decades, and there is now a demand to collaborate and address local need in an innovative way. The aim of this article was to summarize new developments in plastic and reconstructive surgery in global health, to unify our approach to international outreach. Specifically, 5 topics are explored: current models in international outreach, benefits and concerns, the value of research, the value of international surgical outreach education, and the value of technology. A "Let's Reconstruct Global Surgery" network has been formed using Facebook as a platform to unite plastic and reconstructive surgeons worldwide who are interested in international outreach. The article concludes with actionable recommendations from each topic.

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.

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)0.004
metaresearch head score (Gemma)0.022
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.238
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.022
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.045
GPT teacher head0.337
Teacher spread0.292 · 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