Prioritization process for European Academy of Neurology clinical practice guidelines
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The development of high-quality clinical practice guidelines (CPGs) takes substantial time, effort, and resources. During the past years, the European Academy of Neurology (EAN) guideline production was significantly increased, so the need to develop clear, transparent, and methodologically solid criteria for prioritizing guideline topics became apparent. With this paper, we aim to define a set of criteria to be applied for prioritizing topics for future EAN guidelines, as well as the procedure for their implementation. METHODS: After review of the literature, we identified a recent systematic review that reported on the main prioritization criteria used by health organizations. Based on these, we developed a list of 20 preliminary criteria, which were voted on through a Delphi consensus procedure, including 160 stakeholders. Finally, we established a working procedure on how to submit and select new guideline topic proposals within the EAN. This procedure was reviewed by the EAN Scientific Committee and the Board. RESULTS: The first round, 61.3% of the participants voted, and 86% of them participated in the second round. Seven criteria were approved with this procedure. After the selection of the criteria, a prioritization procedure was launched, and the first 30 topics are reported in this paper. This bottom-up process that involved the whole EAN community was followed by a top-down process, using additional criteria for further selection by the EAN board members. CONCLUSIONS: We describe the development of prioritization criteria to be applied in the process of topic selection for future EAN CPGs. We will perform regular reviews and adjustments of the process.
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 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 | 0.386 | 0.457 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.024 | 0.013 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.007 | 0.017 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 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".