Use and misuse of common terminology criteria for adverse events in cancer clinical trials
Bibliographic record
Abstract
BACKGROUND: Common Terminology Criteria for Adverse Events, Version 3.0 (CTCAE v3.0) were released in 2003 and have been used widely to report toxicity in publications or presentations describing cancer clinical trials. Here we evaluate whether guidelines for reporting toxicity are followed in publications reporting randomized clinical trials (RCTs) for cancer. METHODS: Phase III RCTs evaluating systemic cancer therapy published between 2011 and 2013, were reviewed to identify eligible studies, which stated explicitly that CTCAE v3.0 was used to report toxicity. Each AE term and its grade were located in CTCAE v3.0 to determine if they fell within the guidelines provided in the explanatory file. RESULTS: A total of 166 publications were included in this analysis. Criteria from CTCAE v3.0 were frequently used incorrectly. For example, CATEGORY names such as Metabolic were misreported as AEs in 19 trials, and inappropriate grades for AEs assigned frequently. For example, febrile neutropenia was graded 1 or 2 in 35 of 91 studies (38 %), but the minimum grade for this toxicity is 3. Alopecia was graded 3 or more in 19 of 77 studies (25 %), but the maximum is only grade 2. CONCLUSION: The present study provides evidence of poor reporting of toxicity in clinical trials. The study provides a lower estimate for the misuse of AE terms and grades, and implies that other AE terms and grades that conform to CTCAE v3.0 guidelines may have been assigned incorrectly. Inaccurate reporting of toxicity in clinical trials can lead clinicians to make inappropriate treatment decisions.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Reporting · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.615 | 0.801 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.014 |
| Bibliometrics | 0.029 | 0.036 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".