Measuring Quality Care in Localized Renal Cell Cancer: Use of Appropriate Preoperative Investigations in a Population-Based Cohort
Bibliographic record
Abstract
INTRODUCTION: Obtaining appropriate preoperative risk-specific staging investigations for localized renal cell carcinoma (rcc) is a recognized quality indicator. The goal of the present work was to determine the use and appropriateness of preoperative investigations in patients undergoing curative surgery for rcc. METHODS: This population-based retrospective study of patients having surgery for localized rcc recorded the use of preoperative imaging and laboratory investigations within 6 months of surgery. "Appropriate" stage-specific investigations were determined using recognized published guidelines. RESULTS: The study cohort consisted of 544 patients with 72.8% being stage i, 18.4% being stage ii, and 8.8% being stage iii by clinical TNM (2002) criteria. In 61.6%, chest imaging was obtained by chest radiography or computed tomography (ct) within 3 months preoperatively; in 75.6%, such imaging was obtained within 6 months. Abdominal ct imaging was obtained in 97.1% of patients before surgery, with 77.5% of patients receiving such imaging within 3 months of surgery. Complete blood count, electrolytes, and creatinine were measured in 99.1% of patients, but those tests plus other recommended blood tests including calcium, alkaline phosphatase, and liver function were measured in only 17.7%. CONCLUSIONS: In this study, most patients received appropriate abdominal imaging, but chest imaging was underutilized in the overall cohort. Despite being recommended, blood tests such as liver function, alkaline phosphatase, and calcium were completed in fewer than 2 of 10 patients. This analysis provides the groundwork for quality improvement initiatives directed to the use of preoperative investigations in localized rcc.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".