Globalization of oncology clinical trials: Which lower-middle and upper-middle income countries are participating?
Bibliographic record
Abstract
e13512 Background: With globalization of cancer research, many randomized controlled trials (RCTs) led by high income countries (HICs) are now enrolling patients from lower- and upper-middle income countries (LMICs/UMICs). While enrolling diverse global populations promotes research collaborations, there are unanswered questions about which countries participate in RCTs and how this may contribute to global research capacity. Here we describe which UMICs/LMICs participate in RCTs led by HICs. Methods: The study cohort was identified from a database of all oncology RCTs (systemic/surgery/RT) published globally during 2014-2017. The study cohort was restricted to RCTs led by HICs which enrolled participants from LMIC/UMICs. We used a bibliometric approach to explore whether the participation of UMICs/LMICs in RCTs led by HICs was as expected based on other measures of cancer research activity. Country-level bibliometric output for 2007-2017 was identified in the Web of Science database. We compared RCT participation (i.e. % of RCTs in our cohort that each LMIC/UMIC participated in) with country-level cancer research bibliometric output (i.e. % of total cancer research bibliometric output from the same group of countries that came from a specific LMIC/UMIC). Results: The global cohort included 694 RCTs; 636 (92%) of which were led by HICs. Among the HIC-led trials, 187 (29%) enrolled patients in LMICs (n=84) and/or UMICs (n=182); this formed the study cohort. The most common participating LMICs were India (50% of trials, 42/84), Ukraine (46%, 39/84), Philippines (25%, 21/84), and Egypt (14%, 12/84). The most common participating UMICs were Russian Federation (63% of trials, 115/182), Brazil (50%, 91/182), Romania (34%, 61/182), China (31%, 56/182), Mexico (31%,56/182) and South Africa (30%, 54/182). Several LMICs are over-represented in our cohort of RCTs based on proportional cancer research bibliometric output: Ukraine (46% of RCTs but 2% of cancer research bibliometric output), Philippines (25% RCTs, 1% output), Georgia (8% RCTs, 0.2% output). Several UMICs are also over-represented in the study cohort of RCTs including Russia (63% RCTs, 2% output), Romania (34% RCTs, 2% output), Mexico (31% RCTs, 2% output) and South Africa (30% RCTs, 1% output). The inverse relationship was seen for China (31% RCTs, 69% output). Conclusions: A substantial proportion of RCTs led by HICs enroll patients in LMICs/UMICs. The LMICs/UMICs which participate in these trials are not as one would expected based on overall cancer bibliometric output as a surrogate for research ecosystem maturity. Reasons for this apparent discordance and how these data may inform future capacity strengthening activities require further study.
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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 | 0.209 | 0.415 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.031 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".