Benchmarking quality for renal cancer surgery: Canadian Kidney Cancer information system (CKCis) perspective
Bibliographic record
Abstract
INTRODUCTION: There is a lack of validated quality metrics to evaluate the care of patients receiving surgery for renal cell carcinoma (RCC). To address this, the Kidney Cancer Research Network of Canada defined a list of quality indicators (QI) to assess hospital-level performance. We have case-mix adjusted these QIs to benchmark RCC surgical care at Canadian academic centres. METHODS: The Canadian Kidney Cancer information system (CKCis) was used to measure six QIs: laparoscopic approach proportion (LA), partial nephrectomy proportion (PN), partial nephrectomy in patients with chronic kidney disease (CKDPN), positive margin rate (PMR), partial nephrectomy complication rate (PNCx), and warm ischemia time (WIT). To benchmark performance, indirect standardization (observed-to-expected ratio) methodology was employed using multivariate regression models. RESULTS: Multivariate models for LA, PN, and CKDPN demonstrated good discrimination and were used for benchmarking. National averages of 74% (70-78%), 73% (70-75%), and 70% (67-74%) for the LA, PN, and CKDPN QIs, respectively, were determined and used to benchmark individual hospital performance. Overall, three (23%), two (15%), and two (15%) hospitals performed below expected for LA, PN, and CKDPN, respectively. Hospital identity was an independent predictor of LA, PN, and CKDPN (p<0.001). CONCLUSIONS: Significant variability between CKCis hospitals for three RCC surgical QIs exists. Using the CKCis infrastructure may provide a framework for institution-level audit feedback for quality improvement. Greater CKCis capture rates and further data supporting the construct validity of these QIs are required to extend the use of this dataset to real-world quality initiatives.
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".