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Record W2892820243 · doi:10.1097/bot.0000000000001290

Role of North-South Partnership in Trauma Management: Uganda Sustainable Trauma Orthopaedic Program

2018· article· en· W2892820243 on OpenAlexaffabout
Peter J. O’Brien, Isaac Kajja, Jeffrey Potter, Nathan N. O’Hara, Edward Kironde, Brad Petrisor

Bibliographic record

VenueJournal of Orthopaedic Trauma · 2018
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsMcMaster UniversityUniversity of British Columbia
Fundersnot available
KeywordsMedicineGeneral partnershipOrthopedic surgeryMultidisciplinary approachRehabilitationOrthopedic traumaNursingPhysical therapySurgery

Abstract

fetched live from OpenAlex

Uganda, as do many low-middle income countries, has an overwhelming volume of orthopaedic trauma injuries. The Uganda Sustainable Trauma Orthopaedic Program (USTOP) is a partnership between the University of British Columbia, McMaster University and Makerere University that was initiated in 2007. The goal of the project is to reduce the disabilities that occur secondary to musculoskeletal trauma in Uganda. USTOP works with local collaborators to build orthopaedic trauma capacity through teaching, innovation, and research. USTOP has maintained a multidisciplinary approach to training, involving colleagues in anesthesia, nursing, rehabilitation, and sterile reprocessing. The project was initiated at the invitation of the Department of Orthopaedics at Makerere University and Mulago Hospital in Kampala. The project is a collaboration between Canadian and Ugandan orthopaedic surgeons and is driven by the needs identified by the Ugandan surgeons. The program has also worked with collaborators to develop several technologies aimed at reducing the cost of providing orthopaedic care without compromising quality. As orthopaedic trauma capacity in Uganda advances, USTOP strives to continually evolve and provide relevant support to colleagues in Uganda to ensure that changes result in sustainable improvements in patient 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.463
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.294
Teacher spread0.275 · 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 designObservational
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

Citations12
Published2018
Admission routes2
Has abstractyes

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