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Record W2465621137 · doi:10.1097/bot.0000000000000408

Orthopaedic Trauma Care Specialist Program for Developing Countries

2015· article· en· W2465621137 on OpenAlexafffund
Gerard P. Slobogean, Sheila Sprague, Andrew Furey, Andrew N. Pollak

Bibliographic record

VenueJournal of Orthopaedic Trauma · 2015
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsMemorial University of NewfoundlandMcMaster University Medical CentreMcMaster University
FundersCanadian Institutes of Health Research
KeywordsMedicineCurriculumHealth careDeveloping countryTrauma careSustainabilityPoison controlNursingMedical emergencyMedical education

Abstract

fetched live from OpenAlex

The dire challenges faced in Haiti, both preearthquake and postearthquake, highlight the need for developing surgical infrastructure to care for traumatic musculoskeletal injuries. The proposed Orthopaedic Trauma Care Specialist (OTCS) residency program aims to close the critical human resource gap that limits the appropriate care of musculoskeletal trauma in Haiti. The OTCS program is a proposal for a 2-year residency program that will focus primarily on the management of orthopaedic trauma. The proposed program will be a comprehensive approach for implementing affordable and sustainable strategies to improve orthopaedic trauma care. Its curriculum will be tailored to the injuries seen in Haiti, and the treatments that can be delivered within their health care system. Its long-term sustainability will be based on a "train-the-trainers" approach for developing local faculty to continue the program. This proposal outlines the OTCS framework specifically for Haiti; however, this concept is likely applicable to other low- and middle-income environments in a similar need for improved trauma and fracture care.

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)0.002
metaresearch head score (Gemma)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.820
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
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 head0.049
GPT teacher head0.344
Teacher spread0.295 · 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
GenreEmpirical

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

Citations8
Published2015
Admission routes2
Has abstractyes

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