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Record W2789666167 · doi:10.2106/jbjs.16.00723

Global Forum: Spine Research and Training in Underserved, Low and Middle-Income, Culturally Unique Communities: The World Spine Care Charity Research Program’s Challenges and Facilitators

2016· article· en· W2789666167 on OpenAlexaff
O’Dane Brady, Margareta Nordin, Maria Hondras, Geoff Outerbridge, Deborah Kopansky-Giles, Pierre Côté, Sophia da Silva, Timothy Ford, Stefan Eberspaecher, Emre Acaroğlu, Tiro Mmopelwa, Eric L. Hurwitz, Scott Haldeman

Bibliographic record

VenueJournal of Bone and Joint Surgery · 2016
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsOntario Tech UniversityCanadian Memorial Chiropractic CollegeUniversity of Toronto
Fundersnot available
KeywordsNature versus nurtureBusinessResource (disambiguation)Public relationsTraining (meteorology)Low and middle income countriesMedicinePolitical scienceEconomic growthMedical educationGeographyDeveloping countrySociology

Abstract

fetched live from OpenAlex

The World Spine Care (WSC), established by volunteers from 5 continents, is dedicated to providing sustainable, evidence-based spine care to individuals and communities in low and middle-income countries consistent with available health-care resources and integrated within the local culture. The research committee approves and oversees the WSC's collaborative research and training projects worldwide and serves to create a sustainable research community for underserved populations focused on preventing disability from spinal disorders. The purpose of this article is to describe 4 projects overseen by the WSC research committee and to discuss several challenges and specific facilitators that allowed successful completion of initiatives. These novel projects, which involved establishing spine surgery expertise and data collection in the WSC clinics and surrounding communities, all met their aims. This was achieved by overcoming language and resource challenges, adapting to local customs, and taking time to build mutual respect and to nurture relationships with local investigators and stakeholders.

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)0.009
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.390
Threshold uncertainty score0.430

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
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 head0.231
GPT teacher head0.401
Teacher spread0.170 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations22
Published2016
Admission routes1
Has abstractyes

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