Collaborative multicenter trials in Latin America: challenges and opportunities in orthopedic and trauma surgery
Bibliographic record
Abstract
CONTEXT AND OBJECTIVE Orthopedic research agendas should be considered from a worldwide perspective. Efforts should be planned as the means for obtaining evidence that is valid for health promotion with global outreach. DESIGN AND SETTING Exploratory study conducted at Universidade Federal de São Paulo (Unifesp), São Paulo, Brazil, and McMaster University, Hamilton, Canada. METHODS We identified and analyzed collaborative and multicenter research in Latin America, taking into account American and Canadian efforts as the reference points. We explored aspects of the data available from official sources and used data from traffic accidents as a model for discussing collaborative research in these countries. RESULTS The evaluation showed that the proportion of collaborative and multicenter studies in our setting is small. A brief analysis showed that the death rate due to traffic accidents is very high. Thus, it seems clear to us that initiatives involving collaborative studies are important for defining and better understanding the patterns of injuries resulting from orthopedic trauma and the forms of treatment. Orthopedic research may be an important tool for bringing together orthopedic surgeons, researchers and medical societies for joint action. CONCLUSIONS We have indicated some practical guidelines for initiatives in collaborative research and have proposed some solutions with a summarized plan of action for conducting evidence-based research involving orthopedic trauma.
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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 | 0.390 | 0.478 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.006 |
| Bibliometrics | 0.008 | 0.020 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".