Best Practice Methods for Living Evidence Synthesis in Health Care: An International Modified Delphi Survey
Bibliographic record
Abstract
Abstract Background Living evidence syntheses (LES) represent continual updates for an important topic for decision-making where there is uncertainty in the evidence. Whereas best practices for traditional evidence syntheses in health care are well established, they are not for LES. This study aimed to establish globally relevant, agreed-upon standards for considering, conducting, publishing, and implementing LES in health care. Methods A modified Delphi consensus process was conducted. Potential participants were identified through a prior survey, workshop, and targeted outreach based on expertise and representation across global organizations producing or supporting LES. Three modified Delphi rounds were administered using JISC Online Surveys between January and April 2025. Participants rated 23 statements on a five-point Likert scale. Consensus was defined as ≥80% agreement (‘agree’ or ‘strongly agree’) with ≥85% panel response rate required per round. Qualitative feedback guided iterative statement revision. Draft statements were informed by an overview of living systematic reviews, a living evidence survey plus workshop activity, and an ongoing living critical interpretive synthesis of LES. Statements expanded upon the living methodology reporting guidance published by the PRISMA-LSR group to include other considerations for LES. Results The Delphi panel comprised 29 experts from around the world, with 27 (93%) completing round 1, 26 (90%) round 2, and 27 (93%) completing round 3; 19 of 23 statements achieved consensus. Statements described conduct (n=12), including set up and maintenance of living mode as well as funding and resources; reporting (n=2); publishing (n=4); and implementation/appraisal (n=1). Final statements included ways to enable the living mode, such as version history; authoring tools; collaboration; unit of update; transparency; communication; publication considerations; and digital and technological considerations. Conclusions This study presents the first established consensus for best practices in considering, conducting, publishing, and implementing LES in health care. These LES standards can help align global processes, improve transparency, and promote sustainability. But there is still more to be done to accomplish these objectives, requiring that groups collaborate to embrace digital tools, adopt interoperability standards, and create effective appraisal tools.
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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 | 0.479 | 0.389 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".