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Record W2606770558 · doi:10.3389/fpubh.2017.00057

Barriers to Clinical Research in Latin America

2017· article· en· W2606770558 on OpenAlexaff
Kathryn H. Chomsky‐Higgins, Theodore Miclau, Madeline C. MacKechnie, Dino Aguilar, Jorge Rubio Avila, Fernando Baldy dos Reis, Roberto Balmaseda, Antonio Barquet, Alfredo Quiñones Ceballos, Fernando Contreras, Igor Escalante, Nelson Elias, Sergio Iriarte Vincenti, Christian Lozano, Fryda Medina, Gavino Merchan, Julio Segovia, Enrique Guerado, José Eduardo Quintero, Saam Morshed, Mohit Bhandari

Bibliographic record

VenueFrontiers in Public Health · 2017
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsMcMaster University
FundersErasmus Medisch CentrumTeikyo University School of MedicineStryker
KeywordsLatin AmericansGeneral partnershipMedicineHealth careDeveloping countryPolitical scienceEconomic growth

Abstract

fetched live from OpenAlex

Enhancing health research capacity in developing countries is a global health priority. Understanding the orthopedic burden of disease in Latin America will require close partnership between more-developed and less-developed countries. To this end, the Osteosynthesis and Trauma Care Foundation assembled a research consortium of Latin-American orthopedic leaders. Prior to the meeting, we surveyed attendees on perceived barriers to conducting research at their institutions. During the event, working groups discussed these barriers, developed strategies for addressing them, and planned future steps for collaboration. The participants established the need for global relationships that allow colleagues from Latin America to access to training and established investigational infrastructure of North American centers to address research questions relevant to their communities. As a result of the discussion, the International Orthopaedic Multicenter Study (INORMUS) in Fracture Care was initiated. Since then, an expanded international working group, Associación de Cirujanos Traumatológicos en las Americas (ACTUAR), has been created with the purpose of promoting increased global partnership for research capacity development.

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)0.013
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.465
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.221
GPT teacher head0.506
Teacher spread0.284 · 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

Citations43
Published2017
Admission routes1
Has abstractyes

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