The RAND/PPMD Patient-Centeredness Method: a novel online approach to engaging patients and their representatives in guideline development
Bibliographic record
Abstract
Although clinical practice guidelines (CPGs) provide recommendations for how best to treat a typical patient with a given condition, patients and their representatives are not always engaged in CPG development. Despite the agreement that patient participation may improve the quality and utility of CPGs, there is no systematic, scalable method for engaging patients and their representatives, as well as no consensus on what exactly patients and their representatives should be asked to do during CPG development. To address these gaps, an interdisciplinary team of researchers, patient representatives, and clinicians developed the RAND/PPMD Patient-Centeredness Method (RPM) - a novel online approach to engaging patients and their representatives in CPG development. The RPM is an iterative approach that allows patients and their representatives to provide input by (1) generating ideas; (2) rating draft recommendations on two criteria (importance and acceptability); (3) explaining and discussing their ratings with other participants using online, asynchronous, anonymous, moderated discussion boards, and (4) revising their responses if needed. The RPM was designed to be consistent with the RAND/UCLA Appropriateness Method used by clinicians and researchers to develop CPG, while helping patients and their representative rate outcome importance and recommendation acceptability - two key components of the GRADE Evidence to Decision (EtD) framework. With slight modifications, the RPM has the potential to explore consensus among key stakeholders on other dimensions of the EtD, including feasibility, equity, and resource use.
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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 | 0.122 | 0.256 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".