Defining melanoma quality indicators: A modified Delphi approach
Bibliographic record
Abstract
Purpose: To identify consensus-based quality indicators to evaluate melanoma care Methods: Melanoma quality indicators were identified from a literature review.Twenty-nine indicators in six topic domains were subsequently developed: diagnosis and diagnostic biopsy, patient experience, treatment, pathology, symptom management, survivorship.Clinical experts in melanoma from Ontario and international jurisdictions, representing the disciplines of surgery, pathology, primary care, medical oncology, radiation oncology, and palliative care, in addition to a health system leader, an epidemiologist and a patient and family advisor were invited to participate on the Delphi Panel.Panelists were asked to rate indicators on a nine-point Likert scale for appropriateness.Two iterations of electronic surveys were anonymously completed, followed by a virtual consensus meeting to prioritize quality and outcome melanoma indicators.Results: Twenty-three panel members participated in the modified-Delphi process.Twenty-three quality indicators reflecting high-quality melanoma care across the care continuum were prioritized based on a defined consensus agreement of 80% although only 4 were measurable from current administrative databases.These indicators were assessed for measurement feasibility and four of these were deemed feasible to measure from administrative databases along with eight standard indicators. Conclusion:Melanoma outcome and quality indicators were identified using a modified-Delphi process for inclusion in a provincial cancer system performance report.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; a candidate call from one teacher head, 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".