Randomized Controlled Trials in Lung, Gastrointestinal, and Breast Cancers: An Overview of Global Research Activity
Bibliographic record
Abstract
Background: In this study, we compared and contrasted design characteristics, results, and publications of randomized controlled trials (RCTs) in gastrointestinal (GI), lung, and breast cancer. Methods: A PUBMED search identified phase III RCTs of anticancer therapy in GI, lung, and breast cancer published globally during the period 2014−2017. Descriptive statistics, chi-square tests, and the Kruskal−Wallis test were used to compare RCT design, results, and output across the cancer sites. Results: A total of 352 RCTs were conducted on GI (36%), lung (29%), and breast (35%) cancer. Surrogate endpoints were used in 55% of trials; this was most common in breast trials (72%) compared to GI (47%) and lung trials (43%, p < 0.001). Breast trials more often met their primary endpoint (54%) than GI (41%) and lung trials (41%) (p = 0.024). When graded with the ESMO-MCBS, lung cancer trials (50%, 15/30) were more likely to meet the threshold for substantial benefit. GI trials were published in journals with a substantially lower impact factor (IF; median IF 13) than lung (median IF 21) and breast cancer trials (median IF 21) (p = 0.038). Conclusions: Important differences in RCT design and output exist between the three major cancer sites. Use of surrogate endpoints and the magnitude of benefit associated with new treatments vary substantially across cancer sites.
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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 | 0.192 | 0.302 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.021 | 0.029 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".