The International Patient Decision Aid Standards (IPDAS) Collaboration: Evidence Update 2.0
Bibliographic record
Abstract
Established in 2003, the International Patient Decision Aid Standards (IPDAS) Collaboration aims to enhance the quality and effectiveness of patient decision aids (PtDAs) by establishing a shared evidence-informed framework to guide developers and researchers in their development, content, evaluation, and implementation.The original IPDAS checklist, based on evidence syntheses, focused on components of PtDAs known to support informed, values-based reasoning and engagement with health care professionals.In this article, we present IPDAS Evidence Update 2.0.The 13 articles that make up this update provide the latest evidence on 11 core IPDAS domains: development process, 1 providing balanced information, 2 communicating probabilities of outcomes, 3,4 clarifying values, 5 using personal stories, 6 guidance and decision coaching, 7 disclosing conflicts of interest, 8 health literacy, 9,10 basing information on scientific evidence, 11 measuring effectiveness, 12 and implementation of PtDAs.
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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 | 0.045 | 0.159 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.011 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".