Toxicity reporting consistency and subjective minimizing language use in colorectal cancer (CRC) and pancreatic cancer (PaC) clinical trials: A systematic review of phase III randomized controlled trials (RCTs) presented at ASCO between 2012-2022.
Bibliographic record
Abstract
1567 Background: Objective and complete toxicity reporting in clinical trials is critical for patient-centered shared decision-making. Conference abstracts inform initial impressions of practice-changing treatments. Methods: We performed a systematic review of all abstracts of CRC and PaC phase 3 RCTs presented at ASCO annual meetings between 2012 – 2022; long-term follow-up, supportive care, and solely non-pharmacological studies were excluded. Objective minimization of adverse event (AE) reporting was defined as absent and/or incomplete reporting of cumulative grade 3-5 CTCAE (common terminology criteria for AE). We also assessed the use of subjective minimizing language (Chin-Yee et al ASH 2022), defined as use of “acceptable,” “tolerable”, “manageable”, “favorable” (primary minimization terms), or “feasible”, “safe”, “patients did well”, “limited” (secondary minimization terms), terms that falsely imply patients deemed the therapy as such. Presence/absence of PRO or QOL data was also assessed. Results: 63 RCTs met entry criteria (42 CRC, 21 PaC), detailed in Table. Most trials studied chemotherapy +/- other drugs (52; 83%). 17% of all abstracts did not provide any information on AE. Quantitative data on AEs were reported by 38 (60%) of abstracts. However, serious AE reporting was frequently absent (Table), with some trials reporting only specific toxicities (e.g. cytopenias) instead of cumulative CTCAE. Only 7 (11%) of abstracts noted the occurrence or absence of fatal AE. Any subjective-minimizing language was used in 15 (24%) abstracts. Notably, none of the abstracts using subjective-minimizing language provided information on fatal AE rates, nor reported on the patient perspective via QOL or PRO. Average grade ≥ 3 AE in the experimental arm were similar in abstracts with vs without minimizing language (44% vs 45%). Conclusions: Our systematic review of ph 3 RCTs in GI oncology presented at ASCO annual meetings reveals that subjective minimizing language is often used to describe serious toxicities, and without formally assessing the patient voice. Serious AE reporting is frequently absent or incomplete.[Table: see text]
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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 | 0.232 | 0.423 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.018 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".