Using the Delphi Technique to Improve Clinical Outcomes Through the Development of Quality Indicators in Renal Cell Carcinoma
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: Optimal quality of care is needed for ideal outcomes. In renal cell carcinoma (RCC), there is a lack of information defining optimal care. This is particularly important in RCC, with increased complexity of care and a need for coordination among providers. The goal of this study was to identify quality indicators (QIs) and measures of quality care across the RCC disease spectrum. MATERIALS AND METHODS: A modified Delphi technique was used to select QIs that are relevant and practical to RCC care. This technique involved an expert panel of 13 urologic and medical oncologists who participated in two e-mail questionnaires and an in-person meeting to review and prioritize potential QIs. These potential QIs were identified from a systematic literature review or were suggested by panel members. RESULTS: From 233 literature citations, 34 possible QIs were identified; 24 additional potential QIs were suggested. A final set of 23 QIs was established. These are distributed across the RCC disease spectrum as follows (number of QIs in parentheses): screening (n=1), diagnosis/prognosis (n=3), surgical for localized disease (n=6), surgery for advanced disease (n=3), systemic therapy (n=6), and follow-up (n=2). In addition, two QIs related to survival outcomes (overall and progression-free survival) were selected. CONCLUSION: A systematic, consensus-based approach was used to determine relevant QIs in RCC care. These 23 QIs will provide a means of evaluating the quality of RCC care in an effort to improve outcomes in patients. The next step will be to establish a means of measuring each QI based on defined or yet-to-be-defined benchmarks.
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.
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 | 0.033 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it