Toward Minimum Standards for Certifying Patient Decision Aids
Bibliographic record
Abstract
OBJECTIVE: The IPDAS Collaboration has developed a checklist and an instrument (IPDASi v3.0) to assess the quality of patient decision aids (PDAs) in terms of their development process and shared decision-making design components. Certification of PDAs is of growing interest in the US and elsewhere. We report a modified Delphi consensus process to agree on IPDASi (v3.0) items that should be considered as minimum standards for PDA certification, for inclusion in the refined IPDASi (v4.0). METHODS: A 2-stage Delphi voting process considered the inclusion of IPDASi (v3.0) items as minimum standards. Item scores and qualitative comments were analyzed, followed by expert group discussion. RESULTS: One hundred and one people voted in round 1; 87 in round 2. Forty-seven items were reduced to 44 items across 3 new categories: 1) qualifying criteria, which are required in order for an intervention to be considered a decision aid (6 items); 2) certification criteria, without which a decision aid is judged to have a high risk of harmful bias (10 items); and 3) quality criteria, believed to strengthen a decision aid but whose omission does not present a high risk of harmful bias (28 items). CONCLUSIONS: This study provides preliminary certification criteria for PDAs. Scoring and rating processes need to be tested and finalized. However, the process of appraising the quality of the clinical evidence reported by the PDA should be used to complement these criteria; the proposed standards are designed to rate the quality of the development process and shared decision-making design elements, not the quality of the PDA's clinical content.
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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 | 0.533 | 0.630 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.009 | 0.017 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".