The Contents and Readability of Informed Consent Forms for Oncology Clinical Trials
Bibliographic record
Abstract
OBJECTIVES: To compare the quality of informed consent forms (ICF) for different trial phases, funding sources, oncology subspecialties, disease settings, and intervention modalities. METHODS: ICF for prospectively conducted clinical trials were examined for their descriptions of benefits and risks, study alternatives, voluntary participation, and confidentiality. Readability was assessed with Flesch Reading Ease (FRE) score and Flesch-Kincaid Reading Grade Level. RESULTS: Among 262 evaluable trials, ICF contained an average of 3982 words, 379 sentences, and 10.5 pages. The mean FRE score and Reading Grade Level were 61.2 and 7.4, respectively. All ICF explicitly stated that the intervention was investigational. Only 2 (1%) promised direct personal benefits, 16 (6%) suggested the chance of cure or prolonged survival, and 89 (34%) indicated a potential for tumor response. Conversely, 239 (91%) mentioned the risk of serious harms, 217 (83%) admitted that some side effects could be unknown or unpredictable, and 126 (48%) reported hospitalization or death as a possibility. Alternatives to participation, right to withdraw from study, and data confidentiality were addressed in 242 (92%), 254 (97%), and 260 (99%) ICF, respectively. Hematology, industry-funded, metastatic, and systemic therapy trials were most likely to highlight major risks (P < 0.05). Readability was better in phase I trials and in studies, which were performed by medical oncologists, sponsored by governmental agencies, conducted in the metastatic setting, and involved systemic therapy (P < 0.05). CONCLUSIONS: ICF had acceptable readability and provided a realistic overview of the benefits and risks of clinical trials, but the potential for hospitalization or fatality was underreported.
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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 | 0.521 | 0.788 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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, 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".