Determinants of patients' treatment preferences in a clinical trial
Bibliographic record
Abstract
Several researchers have suggested that patients' preferences for a particular form of treatment should be taken into account in clinical trials. Preferences may influence the outcome of treatment, especially in trials when patients cannot be blinded to the type of treatment received and the outcome is based on patients' evaluations of therapy. Participants in this study were 136 edentulous patients who took part in a randomised controlled clinical trial comparing two types of treatments for edentulism: conventional dentures and implant-supported prostheses. Prior to receiving treatment, subjects were required to complete a questionnaire regarding their satisfaction with their present prostheses. In addition, they were asked to indicate which treatment they would prefer if given a choice. The objective of this study was to determine whether there are important differences among study participants between patients who have a treatment preference and those who do not. The effects of satisfaction with pre-treatment prostheses, age, gender and level of education on preferences were examined. Level of satisfaction with the original dentures and level of education were significant predictors of preference. Compared to subjects who rated their satisfaction with their current condition as 'low', the odds ratios associated with having a preference for implant treatment were 0.31 (95% CI: 0.09 to 0.96) for subjects who rated their prostheses in the 'medium' range and 0.11 (95% CI: 0.03 to 0.41) for those who rated in the 'high' range. In addition, subjects with high levels of education were significantly less likely to have a preference for either conventional or implant treatments (OR = 0.18, 95% CI: 0.02 to 0.77 and OR = 0.20, 95% CI: 0.05 to 0.76, respectively) compared to those with low education. Neither age nor gender was a significant predictor of preference. We suggest that study designs which incorporate patients' preferences must take into account possible differences between preference groups that might confound the relationship between preference and the outcome of interest.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; a candidate call from one teacher head, not a consensus.
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".