Current Scenario of Clinical Cancer Research in Latin America and the Caribbean
Bibliographic record
Abstract
In Latin America and the Caribbean (LAC), progress has been made in some national and regional cancer control initiatives, which have proved useful in reducing diagnostic and treatment initiation delays. However, there are still significant gaps, including a lack of oncology clinical trials. In this article, we will introduce the current status of the region's clinical research in cancer, with a special focus on academic cancer research groups and investigator-initiated research (IIR) initiatives. Investigators in LAC have strived to improve cancer research despite drawbacks and difficulties in funding, regulatory timelines, and a skilled workforce. Progress has been observed in the representation of this region in clinical trial development and conduct, as well as in scientific productivity. However, most oncology trials in the region have been sponsored by pharmaceutical companies, highlighting the need for increased funding from governments and private foundations. Improvements in obtaining and/or strengthening the LAC cancer research group's financing will provide opportunities to address cancer therapies and management shortcomings specific to the region. Furthermore, by including this large, ethnic, and genetically diverse population in the world's research agenda, one may bridge the gap in knowledge regarding the applicability of results of clinical trials now mainly conducted in populations from the Northern Hemisphere.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".