Allocation of authorship and patient enrollment among global clinical trials in oncology
Bibliographic record
Abstract
BACKGROUND: Oncology randomized controlled trials (RCTs) are increasingly global in scope. Whether authorship is equitably shared between investigators from high-income countries (HIC) and low-middle/upper-middle incomes countries (LMIC/UMIC) is not well described. The authors conducted this study to understand the allocation of authorship and patient enrollment across all oncology RCTs conducted globally. METHODS: A cross-sectional retrospective cohort study of phase 3 RCTs (published 2014-2017) that were led by investigators in HIC and recruited patients in LMIC/UMIC. FINDINGS: During 2014-2017, 694 oncology RCTs were published; 636 (92%) were led by investigators from HIC. Among these HIC-led trials, 186 (29%) enrolled patients in LMIC/UMIC. One-third (33%, 62 of 186) of RCTs had no authors from LMIC/UMIC. Forty percent (74 of 186) of RCTs reported patient enrollment by country; in 50% (37 of 74) of these trials, LMIC/UMIC contributed <15% of patients. The relationship between enrollment and authorship proportion is very strong and is comparable between LMIC/UMIC and HIC (Spearman's ρ LMIC/UMIC 0.824, p < .001; HIC 0.823, p < .001). Among the 74 trials that report country enrollment, 34% (25 of 74) have no authors from LMIC/UMIC. CONCLUSIONS: Among trials that enroll patients in HIC and LMIC/UMIC, authorship appears to be proportional to patient enrollment. This finding is limited by the fact that more than half of RCTs do not report enrollment by country. Moreover, there are important outliers as a significant proportion of RCTs had no authors from LMIC/UMIC despite enrolling patients in these countries. The findings in this study reflect a complex global RCT ecosystem that still underserves cancer control outside high-income settings.
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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 | 0.334 | 0.584 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".