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Record W2461165364 · doi:10.1186/s12885-016-2408-9

Use and misuse of common terminology criteria for adverse events in cancer clinical trials

2016· article· en· W2461165364 on OpenAlexaff
Sheng Zhang, Fei Liang, Ian F. Tannock

Bibliographic record

VenueBMC Cancer · 2016
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
FundersFudan University
KeywordsCommon Terminology Criteria for Adverse EventsMedicineClinical trialAdverse effectTerminologyMEDLINERandomized controlled trialNeutropeniaIntensive care medicineFamily medicineToxicityInternal medicine

Abstract

fetched live from OpenAlex

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 armCategoriesStudy designConfidence
gemmaMetaresearch
Domain: Reporting · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptMetaresearch
Domain: Methods · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
models agreeAgreement compares identical category sets and study designs across arms.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.088
metaresearch head score (Gemma)0.064
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0880.064
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.948
GPT teacher head0.698
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Labeled directly by 2 models reading the full record.

Study designObservational
DomainReporting · Methods
GenreEmpirical

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".

Quick stats

Citations59
Published2016
Admission routes1
Has abstractyes

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