Involvement of low- and middle-income countries (LMCs) in oncology randomized controlled trials (RCTs).
Bibliographic record
Abstract
6087 Background: Lower costs and fewer regulatory barriers make clinical trials in LMCs attractive, but little is known about characteristics of such trials and roles of investigators from LMCs. We describe trends in participation of investigators from LMCs in oncology RCTs over a decade. Methods: We used Medline to identify all RCTs published in English between January 1 1998 and December 31 2008 evaluating treatment in five solid tumours (lung, breast, colorectal, stomach, liver) with high mortality in LMCs as per GLOBOCAN 2002. Data on author affiliations and roles (first, middle or corresponding author), trial characteristics, funding and study interventions were abstracted using an electronic form. Countries were stratified into low-, middle- and high-income using World Bank data. Interventions were categorized as requiring basic, limited, enhanced or maximal resources as per the Breast Health Global Initiative classification. Logistic regression identified factors associated with investigator participation from LMCs. Results: 454 trials were identified: lung (n=177), breast (n=165), colorectal (n=82), stomach (n=29), liver (n=7). The annual number of trials published increased from 10 trials in 1998 to 86 in 2008. The proportion of trials with at least one LMC author also increased over time from 20% in 1998 to 29% in 2008 (p=0.01), but almost all LMC authors were from middle-income countries. In 17% of trials involving LMC authors, they were first or corresponding authors. 67% of trials involving LMC investigators evaluated interventions requiring enhanced or maximal resources for implementation. Factors associated with LMC participation included industry funding (OR=3.57, p<0.0001), use of placebo arm (OR=2.59, p=0.02) and metastatic setting (OR=3.87, p=0.0003). Conclusions: An increasing number of oncology RCTs involve LMC investigators primarily in non-leadership roles. These trials are commonly industry-funded and often test interventions that require at least enhanced resources for implementation. To minimize concerns of exploitation and facilitate future collaboration it is crucial that interventions are locally feasible and investigators receive appropriate authorship.
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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 | 0.638 | 0.757 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.015 | 0.023 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".