Combining Multiple Treatment Comparisons with Personalized Patient Preferences: A Randomized Trial of an Interactive Platform for Statin Treatment Selection
Bibliographic record
Abstract
BACKGROUND: Patients and clinicians are often required to make tradeoffs between the relative benefits and harms of multiple treatment options. Combining network meta-analysis results with user preferences can be useful when choosing among several treatment alternatives. OBJECTIVE: Using cholesterol-lowering statin drugs as a case study, we aimed to determine whether an interactive web-based platform that combines network meta-analysis findings with patient preferences had an effect on the decision-making process in a general population sample. METHOD: This was a pilot parallel randomized controlled trial. We used Amazon's Mechanical Turk to recruit adults residing in the United States. A total of 349 participants were randomly allocated to view either the interactive tool (intervention) or a series of bar charts (control). The primary endpoint was decisional conflict, and secondary endpoints included decision self-efficacy, preparation for decision making, and the overall ranking of statins. RESULTS: A total of 258 participants completed the trial and were included in the analysis. On the primary outcome, participants randomized to the interactive tool had significantly lower levels of decisional conflict than those in the control group (difference, -8.53; 95% confidence interval [CI], -12.96 to -4.11 on a 100-point scale; P = 0.001). They also appeared to have higher levels of preparation for decision making (difference, 4.19; 95% CI, -0.24 to 8.63 on a 100-point scale; P = 0.031). No difference was found for decision self-efficacy, although groups were statistically significantly different in how they ranked different statins. CONCLUSION: The findings of our proof-of-concept evaluation suggest that an interactive web-based tool combining published clinical evidence with individual preferences can reduce decisional conflict and better prepare individuals for decision making.
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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; a candidate call from one source (direct Gemma or distilled Codex), 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".