A Critical Appraisal of Evidence- and Consensus-Based Guidelines for Actinic Keratosis
Bibliographic record
Abstract
Actinic keratoses (AK) are common lesions of the skin that can be effectively treated with several lesion- and field-directed treatments. Clinical practice guidelines assist physicians in choosing the appropriate treatment options for their patients. Here, we aimed to systematically identify and evaluate the methodological quality of currently available guidelines for AK. Guidelines published within the last 5 years were identified in a systematic search of guideline databases, Medline and Embase. Then, six independent reviewers evaluated the methodological quality using the tools "Appraisal of Guidelines for Research and Evaluation" (AGREE II) and "Recommendation EXcellence" (AGREE-REX). The Kruskal-Wallis (H) test was used to explore differences among subgroups and Spearman's correlation to examine the relationship between individual domains. Three guidelines developed by consortia from Canada, Germany and the United Kingdom were eligible for the evaluation. The German guideline achieved the highest scores, fulfilling 65 to 92% of the criteria in AGREE II and 67 to 84% in AGREE-REX, whereas the Canadian guideline scored 31 to 71% of the criteria in AGREE II and 33 to 46% in AGREE-REX. The domains "stakeholder involvement" and "values and preferences" were identified as methodological weaknesses requiring particular attention and improvement in future guideline efforts.
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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 | 0.341 | 0.659 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.038 | 0.021 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".