Determining the Impact of Informed Choice
Bibliographic record
Abstract
BACKGROUND: The Rucker 2-stage randomized trial (RCT) design and method allows treatment, preference, and selection effects to be estimated separately in clinical trials. OBJECTIVE: To understand the effect of patient choice on patient outcomes, the authors applied the Rucker design and analysis method. DESIGN: They used data from a trial of management strategies for women with atypical cells of undetermined significance (ASCUS) detected at routine cervical screening, in which informed choice using a decision aid was compared to no choice. SETTING: Women's health clinics across Australia. PATIENTS: Women aged 18 to 70 years (n = 314) with ASCUS. INTERVENTION: Women were randomized to either an informed choice of human papillomavirus (HPV) triage testing or repeat Pap testing or to no choice with random allocation to management by either option. MEASUREMENTS: Health-related quality of life (SF36) and satisfaction were measured over the course of management and up to 1 year after triage. RESULTS: Using the Rucker analysis, patients who received their choice had higher quality of life scores than those who did not choose (SF36 MCS, 6% higher, 6.0; 95% confidence interval: -0.6 to 12.9; P = 0.07; effect size 0.61 [moderate]). In contrast, the traditional RCT analysis suggested there was little difference in quality of life between the choice and no-choice trial arms. LIMITATIONS: The Rucker method assumes that the declared preferences for treatment in the choice arm are representative of the preferences that would have been observed in the no-choice arms if choice was available. CONCLUSIONS: The Rucker method should be used to estimate treatment, preference, and selection effects in randomized trials, as it adds to our understanding of the effect of choice on patient outcomes.
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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 | 0.309 | 0.569 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 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".