Barriers to Clinical Research in Latin America
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.287 | 0.324 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".