An Evaluation of Sex- and Gender-Based Analyses in Oncology Clinical Trials
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to evaluate whether sex- and gender-based analyses and proper sex and gender terminology were used in oncology trials leading to regulatory drug approval. METHODS: The Food and Drug Administration (FDA) Hematology/Oncology Approvals and Safety Notifications page was used to identify all anticancer therapies that received FDA approval between 2012 and 2019. The trials used to support FDA drug approval were collected along with all available supplemental tables and study protocols. Documents were reviewed to determine if there was a plan to analyze results according to sex and gender and to determine if consistent sex and gender terminology were used. RESULTS: We identified 128 randomized, controlled trials corresponding to a cancer medicine, which received FDA approval. No study specified how sex and gender were collected or analyzed. No study reported any information on the gender of participants. Sex and gender terminology were used inconsistently at least once in 76% (97 of 128) of studies. Among the 102 trials for nonsex-specific cancer sites, 89% (91 of 102) presented disaggregated survival outcome data by sex. No study presented disaggregated toxicity data by sex or gender. CONCLUSION: The majority of pivotal clinical trials in oncology fail to account for the important distinction between sex and gender and conflate sex and gender terminology. More rigor in designing clinical trials to include sex- and gender-based analyses and more care in using sex and gender terms in the cancer literature are needed. These efforts are essential to improve the reproducibility, generalizability, and inclusiveness of cancer research.
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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 | 0.747 | 0.864 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.020 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".