Prioritizing questions and topics for the development of guidelines and consensus‐based recommendations supported by <scp>ILAE</scp> : A scoping review and proposal of prioritization criteria
Bibliographic record
Abstract
Clinical practice guidelines (CPGs) and consensus-based recommendations (CBRs) require considerable effort, collaboration, and time-all within the constraints of finite resources. Professional societies, such as the International League Against Epilepsy (ILAE), must prioritize what topics and questions to address. Implementing evidence-based care remains a crucial challenge in clinical practice. Using rigorous processes to ensure that the best available research evidence informs health care recommendations is of the utmost importance. We aimed to develop a structured and transparent process for prioritizing future CPGs and CBRs supported by the ILAE. A multidisciplinary group of researchers and experts from the ILAE Prioritization Task Force conducted a scoping review to identify prioritization approaches for CPG and CBR development. This scoping review was reported according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) and Cochrane recommendations. A Problem/population, Concept, and Context (PCC) strategy was applied to the literature search and selection of the studies. We searched Medline/PubMed, Embase, Web of Science, and Scopus without time or language limits. The findings were synthesized qualitatively. A consensus-based process was followed to develop a prioritization scoring tool for CPGs and another for CBRs. Thirty-nine participants, including clinicians, experts in the field, methodologists, and other relevant stakeholders, contributed to developing the final instrument (based on a 5-point Likert scale). Of 721 unique citations, 8 papers reporting prioritization approaches for guideline development were included. Based on these, we developed an initial tool with 10 criteria. It was iteratively optimized and revised by the ILAE Standards and Best Practice Council, which unanimously approved the instrument. The ILAE Executive Committee subsequently approved its final version. The ILAE Prioritization Tool is intended to standardize the prioritization processes and optimize the ILAE's use of resources to select CPGs and CBRs for endorsement.
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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 | 0.475 | 0.599 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.012 | 0.015 |
| Bibliometrics | 0.050 | 0.040 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.011 | 0.018 |
| Research integrity | 0.015 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".