Guidelines for uveal melanoma: a critical appraisal of systematically identified guidelines using the AGREE II and AGREE-REX instrument
Bibliographic record
Abstract
PURPOSE: Clinical practice guidelines provide recommendations for the management of diseases. In orphan conditions such as uveal melanoma (UM), guideline developers are challenged to provide practical and useful guidance even in the absence of high-quality evidence. Here, we assessed the methodological quality and identified deficiencies of international guidelines on UM as a base for future guideline development. METHODS: A systematic search was carried out in guideline databases, Medline and Embase until 27th May 2019 for guidelines on UM published between 2004 and 2019. Five independent reviewers assessed the methodological quality of the identified guidelines using the instruments "Appraisal of Guidelines for Research and Evaluation II" (AGREE II) and AGREE-REX (Recommendation EXcellence). Descriptive analysis was performed and subgroup differences were explored with the Kruskal-Wallis (H) test. The relationship between the individual domains and items of the instruments were examined using Spearman's correlation. RESULTS: Five guidelines published from 2014 to 2018 by consortia of the United States of America, Canada and the United Kingdom (UK) were included. The highest scores were obtained by the UK guideline fulfilling 48-86% of criteria in AGREE II and 30-60% for AGREE-REX. All guidelines showed deficiencies in the domains "editorial independence", "applicability", and "recommendation". Subgroup differences were identified only for the domain "editorial independence". CONCLUSION: The UK guideline achieved the highest scores with both instruments and may serve as a basis for future guideline development in UM. The domains "editorial independence", "recommendation", and "applicability" were identified as methodological weaknesses and require particular attention and improvement in future guidelines.
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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 | 0.243 | 0.588 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.044 | 0.030 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".