Systematic evaluation of Merkel cell carcinoma clinical practice guidelines using the AGREE II instrument
Bibliographic record
Abstract
Merkel cell carcinoma (MCC) is a rare type of skin cancer that requires a multidisciplinary approach with a variety of specialists for management and treatment. Clinical practice guidelines (CPGs) have recently been established to standardize management algorithms. The objective of this study was to appraise such CPGs via the Appraisal of Guidelines for Research and Evaluation (AGREE II) instrument. Eight CPGs were identified via systematic literature search following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. Four appraisers trained in AGREE II protocols evaluated each CPG and deemed two CPGs as high quality, five as moderate quality, and one as low quality. Intraclass correlation coefficients (ICCs) were calculated to verify reviewer consistency as excellent, good, and moderate across four, one, and one domain, respectively. The majority of MCC CPGs are lacking in specifying stakeholder involvement, applicability, and rigor of development. The two high quality CPGs are from the Alberta Health Services (AHS) and the collaboration between the European Dermatology Forum, the European Association of Dermato-Oncology, and the European Organization of Research and Treatment of Cancer (EDF/EADO/EORTC). The EDF/EADO/EORTC CPG had the highest overall score with no significant deficiencies across any domain. An important limitation is that the AGREE II instrument is not designed to evaluate the validity of each CPG's recommendations; conclusions therefore can only be drawn about each CPG's developmental quality. Future MCC CPGs may benefit from garnering public perspectives, inviting external expert review, and considering available resources and implementation barriers during their developmental stages.
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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 | 0.308 | 0.623 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.017 |
| Bibliometrics | 0.033 | 0.024 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".