What is a clinical practice guideline? A roadmap to their development. Special report from the Guidelines Task Force of the International League Against Epilepsy
Bibliographic record
Abstract
Clinical practice guidelines (CPGs) are statements that provide evidence-based recommendations aimed at optimizing patient care. However, many other documents are often published as "guidelines" when they are not; these documents, although also important in clinical practice, are usually not systematically produced following rigorous processes linking the evidence to the recommendations. Specifically, the International League Against Epilepsy (ILAE) guideline development toolkit aims to ensure that high-quality CPGs are developed to fill knowledge gaps and optimize the management of epilepsy. In addition to adhering to key methodological processes, guideline developers need to consider that effective CPGs should lead to improvements in clinical processes of care and health care outcomes. This requires monitoring the effectiveness of epilepsy-related CPGs and interventions to remove the barriers to epilepsy CPG implementation. This article provides an overview of what distinguishes quality CPGs from other documents and discusses their benefits and limitations. We summarize the recently revised ILAE CPG development process and elaborate on the barriers and facilitators to guideline dissemination, implementation, and adaptation.
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 | 0.101 | 0.260 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.014 | 0.020 |
| Insufficient payload (model declined to judge) | 0.010 | 0.009 |
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".