285 Updating the evidence for the IPDAS standards: results from a network meta-analysis
Bibliographic record
Abstract
Introduction To update standards for quality and effectiveness of patient decision aids (PtDAs), the International Patient Decision Aid Standards (IPDAS) Collaboration reviewed the evidence supporting the current criteria. Evidence Update 2.0 reviews representing 11 core IPDAS domains were published in 2021. Here we report findings from the Network Meta-Analysis (NMA) used to inform voting on new or revised IPDAS criteria. Methods NMA was conducted using RCTs from the 2024 Cochrane Review of PtDAs. Six outcome measures were identified for consideration in the NMA (knowledge, accurate risk perceptions, feeling uninformed, unclear values, and feeling undecided). Domain teams submitted questions for the NMA to inform voting on proposed IPDAS criteria changes. NMA was used to test each question by comparing PtDAs with and without specific attributes and to usual care. Results 149/209 RCTs reported outcomes of interest for NMA and 113 PtDAs were available. No differences were found for PtDAs with explicit vs implicit values clarification. PtDAs that included a risk calculator were associated with poorer knowledge, compared to PtDAs without calculators (MD=2.87, 95%CrI 0.06, 5.67). PtDAs used during consult, compared to independent of consult, were associated with poorer knowledge (MD=-4.34, 95%CrI -7.24, -1.43) and more feeling uninformed (5.07, 95%CrI 1.06, 9.11). Involvement of patients/consumers in the development of PtDAs was associated with better knowledge compared to PtDAs developed only by health care providers (MD=6.56, 95%Crl 1.10,12.03). Discussion Evidence from NMAs confirmed the importance of certain attributes of PtDAs. Statistically significant results need to be interpreted in the context of minimal clinically important differences. Conclusions Results from the NMA will be used to inform voting on new or revised criteria. As evidence about the effectiveness of PtDAs continues to expand there will be future opportunities to consider other questions of interest to developers, researchers, and knowledge users.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.207 | 0.395 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.027 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".