Improving outcomes through the development of quality indicators in renal cell cancer.
Bibliographic record
Abstract
422 Background: Optimal quality of care is necessary for ideal outcomes, and quality indicators (QI) are increasingly being used to measure quality of care. In renal cell carcinoma (RCC), there is a paucity of information defining such optimal care. This is particularly important as care of RCC patients is becoming increasingly complicated with more options and requiring greater expertise. The goal of this study was to identify QI for RCC across the entire disease spectrum from presentation to palliation. Methods: A multidisciplinary expert panel (13 members) of medical and urologic oncologists from across Canada reviewed potential QI. These potential QI were identified from a systematic review of the literature. In addition, panel members were encouraged to suggest additional potential QI. A modified Delphi technique was utilized to select QI that were both relevant and practical to RCC; this technique incorporated 2 email questionnaires and 1 in-person meeting. Results: From 250 citations in the systematic review, 34 possible QI were identified; 24 additional potential QI were suggested by panel members. A final set of 23 QI were established by the expert panel. These were distributed across the RCC disease spectrum as follows (number of QI in parentheses): screening (1), diagnosis and prognosis (3), management of localized disease (7), surgical management of locally advanced or metastatic disease (3), systemic therapy (4), and follow-up (3). These 21 QI focused largely on the treatment of RCC. In addition, two QI related to survival outcomes (overall and progression-free) were selected. An example of a QI in localized disease is the proportion of patients undergoing partial nephrectomy for tumors < 4 cm. An example in advanced disease is the proportion of patients who are assessed by members of a multidisciplinary genitourinary cancer team. The final 23 QI selected will be presented in detail. Conclusions: A systematic, consensus-based approach was used to determine relevant QI in RCC care. These 23 QIs will provide a means of evaluating the quality of RCC care in an effort to improve outcomes for our patients. The next step will be to establish a means of measuring each of these QI based on defined or yet to be defined benchmarks.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".