Involvement of low- and middle-income countries in randomized controlled trial publications in oncology
Bibliographic record
Abstract
BACKGROUND: We describe trends in participation by investigators from low- and middle-income countries (LMCs) in publications describing oncology randomized control trials (RCTs) over a decade. METHODS: We used Medline to identify RCTs published in English from 1998 to 2008 evaluating treatment in lung, breast, colorectal, stomach and liver cancers. Data on author affiliations, authorship roles, trial characteristics, funding and interventions were extracted from each article. Countries were stratified as low-, middle- or 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 was used to identify factors associated with authorship by investigators from LMCs. RESULTS: 454 publications were identified. Proportion of articles with at least one LMC author increased over time from 20% in 1998 to 29% in 2008 (p = 0.01), but almost all LMC authors were from middle-income countries. Proportion of articles with at least one LMC author was higher among articles that explicitly reported recruitment in at least one LMC vs those that did not (76% vs 13%). Among 87 articles (19%) that involved authors from LMCs, 17% had LMC authors as first or corresponding authors, and 67% evaluated interventions requiring enhanced or maximal resources. Factors associated with LMC authorship included industry funding (OR = 3.54, p = 0.0001), placebo comparator arm (OR = 2.57, p = 0.02) and palliative intent treatment (OR = 4.00, p = 0.0003). CONCLUSION: An increasing number of publications describing oncology RCTs involve authors from LMC countries but primarily in non-leadership roles in industry-funded trials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.238 | 0.451 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.045 | 0.048 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".