Do Patient Decision Aids Meet Effectiveness Criteria of the International Patient Decision Aid Standards Collaboration? A Systematic Review and Meta-analysis
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: To describe the extent to which patient decision aids (PtDAs) meet effectiveness standards of the International Patient Decision Aids Collaboration (IPDAS). DATA SOURCES: Five electronic databases (to July 2006) and personal contacts (to December 2006). RESULTS: Among 55 randomized controlled trials, 38 (69%) used at least 1 measure that mapped onto an IPDAS effectiveness criterion. Measures of decision quality were knowledge scores (27 trials), accurate risk perceptions (12 trials), and value congruence with the chosen option (3 trials). PtDAs improved knowledge scores relative to usual care (weighted mean difference [WMD] = 15.2%, 95% confidence interval [CI] = 11.7 to 18.7); detailed PtDAs were somewhat more effective than simpler PtDAs (WMD = 4.6%, 95% CI = 3.0 to 6.2). PtDAs with probabilities improved accurate risk perceptions relative to those without probabilities (relative risk = 1.6, 95% CI = 1.4 to 1.9). Relative to simpler PtDAs, detailed PtDAs improved value congruence with the chosen option. Only 2 of 6 IPDAS decision process criteria were measured: feeling informed (15 trials) and feeling clear about values (13 trials). PtDAs improved these process measures relative to usual care (feeling uninformed WMD = -8.4, 95% CI = -11.9 to -4.8; unclear values WMD = -6.3, 95% CI = -10.0 to -2.7). There was no difference in process measures when detailed and simple PtDAs were compared. CONCLUSIONS: PtDAs improve decision quality and the decision process's measures of feeling informed and clear about values; however, the size of the effect varies across studies. Several IPDAS decision process measures have not been used. Future trials need to use a minimum data set of IPDAS evaluation measures. The degree of detail PtDAs require for positive effects on IPDAS criteria should be explored.
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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 | 0.013 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.002 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it