Guidelines for Designing and Conducting Delphi Consensus Studies: An Expert Consensus Delphi Study
Bibliographic record
Abstract
PURPOSE: To conduct a Delphi project to develop guidelines for the design and execution of Delphi studies within medical and surgical specialties. METHODS: Open-ended questions in round 1 and open-ended and semi-open questions in round 2 were answered. The results of the first 2 rounds were used to develop a Likert-style questionnaire for round 3. The level of agreement and consensus was defined as 80%. Consensus was further categorized into specific percentage ranges for clarity: 100% unanimous consensus, 90% to 99% very strong consensus, and 80% to 89% consensus. RESULTS: Consensus was achieved for 35 of 63 items (56%). Unanimous agreement was reached for 4 items (6.3%), while very strong consensus was established for 12 items (19%). Consensus was reached for an additional 19 items (30.1%), and the panel remained undecided on 7 items (11.1%). CONCLUSIONS: Unanimous agreement was reached for iteration, the ability to establish treatment guidelines, a proven track record of panel members, and the requirement for at least 1 steering committee member to be a Delphi expert. Very strong consensus was reached on several key requirements: a clear definition of consensus, controlled feedback between rounds, precise definitions of expert and expertise, and the need for panel members to show experience through publications and clinical practice. Criteria for panel selection should ensure diversity and specialization, with steering committee members being content experts and a minimum of 20 to 30 panel members for broader topics. Regional experts should provide consensus on specific topics only. The steering committee should develop questions, with open-ended questions in round 1 and both types in round 2. Limiting the process to 3 rounds is advisable, aiming for at least 80% consensus in the final round. LEVEL OF EVIDENCE: Level V, expert opinion.
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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 | 0.449 | 0.417 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.010 |
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".