Sex differences in adverse event reporting in SWOG chemotherapy, biologic/immunotherapy, and targeted agent cancer clinical trials.
Bibliographic record
Abstract
11588 Background: Women have more adverse events (AEs) from chemotherapy than men, but few studies have explored sex differences in biologic/immunotherapies (BIs) or targeted therapies. We examined subjective (symptomatic) and objective AEs by sex across different treatments. Methods: We analyzed drug-related severe (grade 3) or worse AEs by sex in SWOG phase II and III clinical trials conducted between 1980-2018, excluding sex-specific cancers. AE codes and grade were categorized using the Common Terminology Criteria for Adverse Events (CTCAE). Subjective or symptomatic toxicities were defined as those aligned with the NCI’s new Patient-Reported Outcome (PRO) CTCAE; lab-based or physician-determined AEs were designated as objective. Multivariable logistic regression was used, adjusting for age, race, and disease prognosis. Thirteen symptomatic and 19 objective AE categories were examined. Results: In total, 36,397 patients (women, 13,907 [38.2%]; men, 22,490 [61.8%]) experiencing 522,835 AEs on 297 trials with 385 treatment arms were analyzed. Overall, 29.1% (n = 10.860) had severe or worse toxicity. Women experienced an increased risk of severe symptomatic AEs for BIs (OR = 1.53, 95% CI: 1.32-1.78, p < .0001), chemotherapy (OR = 1.31, 95% CI: 1.24-1.39, p < .0001), and targeted therapies (OR = 1.23, 95% CI: 1.06-1.43, p = .008). Women also had an increased risk of severe objective AEs for BIs (OR = 1.53, 95% CI: 1.32-1.78, p < .0001), chemotherapy (OR = 1.35, 95% CI: 1.28-1.43, p < .0001), but not targeted therapies (OR = 1.08, 95% CI: 0.94-1.25, p = .28). Across all treatments, sex differences were greater for hematologic (OR = 1.29, 95% CI: 1.24-1.35, p < .0001) v. non-hematologic (OR = 1.13, 95% CI: 1.08-1.18, p < .0001) objective AEs. Conclusions: The greater severity of both symptomatic and objective – especially hematologic – AEs in women across multiple treatment paradigms indicates broad-based sex-differences exist. This could be due to AE reporting, pharmacogenomics of drug metabolism and disposition, total dose received, and/or adherence to therapy. Particularly large sex differences were observed for patients receiving BIs, suggesting studying AEs from these agents is a priority.
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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 | 0.057 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".