Processes for evidence summarization for patient decision aids: A Delphi consensus study
Bibliographic record
Abstract
BACKGROUND: Patient decision aids (PDAs) should provide evidence-based information so patients can make informed decisions. Yet, PDA developers do not have an agreed-upon process to select, synthesize and present evidence in PDAs. OBJECTIVE: To reach the consensus on an evidence summarization process for PDAs. DESIGN: A two-round modified Delphi survey. SETTING AND PARTICIPANTS: A group of international experts in PDA development invited developers, scientific networks, patient groups and listservs to complete Delphi surveys. DATA COLLECTION: We emailed participants the study description and a link to the online survey. Participants were asked to rate each potential criterion (omit, possible, desirable, essential) and provide qualitative feedback. ANALYSIS: Criteria in each round were retained if rated by >80% of participants as desirable or essential. If two or more participants suggested rewording, reordering or merging, the steering group considered the suggestion. RESULTS: Following two Delphi survey rounds, the evidence summarization process included defining the decision, reporting the processes and policies of the evidence summarization process, assembling the editorial team and managing (collect, manage, report) their conflicts of interest, conducting a systematic search, selecting and appraising the evidence, presenting the harms and benefits in plain language, and describing the method of seeking external review and the plan for updating the evidence (search, selection and appraisal of new evidence). CONCLUSION: A multidisciplinary stakeholder group reached consensus on an evidence summarization process to guide the creation of high-quality PDAs. PATIENT CONTRIBUTION: A patient partner was part of the steering group and involved in the development of the Delphi survey.
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 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 | 0.001 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".