Comment on: Two decades of surgical randomized controlled trials: worldwide trends in volume and methodological quality
Bibliographic record
Abstract
Dear Editor RCTs represent the gold standard for evidence generation. Pronk et al.1 reviewed surgical RCTs over a span of 20 years, finding an improvement in trial quality along with a rise in Asia, mostly led by China. Pronk et al.1 focused on progress in reported methodology and study design, but did not consider improvements in terms of equity and, consequently, the generalizability of current trials, which ought to be considered a form of quality. Inequity may present at the trialist level and among patients enrolled in RCTs, both commonly disproportionately dominated by white men in high-income countries. Pronk et al.1 showed that, despite some geographical shift, only one lower-middle-income country (Egypt) was featured in the top 10 in terms of trial volume in 2019, whereas Africa and South America together represented the smallest number of trials at 6.8 per cent of the total volume, despite being home to nearly one-quarter of the world’s population. This challenges the generalizability of trials, as interventions that work within a given country or region do not account for cultural, political, and economical factors that may influence behaviours and outcomes elsewhere. Further, under-enrolment of women and minoritized and racialized populations has been found among trials in high-income countries. Thus, even results within the same country may be difficult to generalize to its entire population. Barriers to improving equity in RCTs are manifold, including high costs, complex logistics, long timelines, and difficulties with data infrastructure and follow-up, which render these opportunities less accessible to researchers in variable-resource contexts, where context-appropriate evidence is especially highly needed. Moreover, imbalances in trialist networks perpetuate biases that result in under-enrolment of under-represented populations. Future directions should include increased mentorship and collaboration to empower early-career trialists, more representative trial teams, and more pragmatic and lower-cost solutions to RCTs. Xiya Ma (Conceptualization, Writing—original draft, Writing—review & editing), and Dominique Vervoort (Conceptualization, Methodology, Supervision, Writing—original draft, Writing—review & editing).
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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 | 0.046 | 0.267 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.115 | 0.062 |
| Insufficient payload (model declined to judge) | 0.008 | 0.011 |
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".