Acceptability of cancer chemoprevention trials: impact of the design
Bibliographic record
Abstract
BACKGROUND: Chemoprevention could significantly reduce cancer burden. Assessment of efficacy and risk/benefit balance is at best achieved through randomized clinical trials. METHODS: At a periodic health examination center 1463 adults were asked to complete a questionnaire about their willingness to be involved in different kinds of preventive clinical trials. RESULTS: Among the 851 respondents (58.2%), 228 (26.8%) agreed to participate in a hypothetical chemoprevention trial aimed at reducing the incidence of lung cancer and 116 (29.3%) of 396 women agreed to a breast cancer chemoprevention trial. Randomization would not restrain participation (acceptability rate: 87.7% for lung cancer and 93.0% for breast cancer). In these volunteers, short-term trials (1 year) reached a high level of acceptability: 71.5% and 73.7% for lung and breast cancer prevention respectively. In contrast long-term trials (5 years or more) were far less acceptable: 9.2% for lung cancer (OR=7.7 CI(95%) 4.4-14.0) and 10.5 % for breast cancer (OR=6.9 CI(95%) 3.2-15.8). For lung cancer prevention, the route of administration impacts on acceptability with higher rate 53.1% for a pill vs. 7.9% for a spray (OR=6.7 CI(95%) 3.6-12.9). CONCLUSION: Overall healthy individuals are not keen to be involved in chemo-preventive trials, the design of which could however increase the acceptability rate.
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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 | 0.654 | 0.738 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".