The impact of robotic surgery access on the management of patients with clinical stage I kidney tumors at Canadian academic centers
Bibliographic record
Abstract
INTRODUCTION: Robotic surgery is used in the treatment of kidney tumors. We aimed to determine if robotic access was associated with initial choice of management for patients with a clinical stage I kidney mass. METHODS: Patients with a clinical stage I kidney mass were identified from the Canadian Kidney Cancer information system (CKCis) cohort. Sites were classified by year and access to robotic surgery. Associations between robotic access and initial management were determined using logistic regression. Univariable and multivariable analyses were performed, adjusting for tumor size and stage, and presented as relative risks (RR ) or adjusted RR (aRR) and 95% confidence intervals (CI). RESULTS: Overall, 4160 patients were included. Among patients treated with surgery, the proportion of partial nephrectomy compared to radical nephrectomy was significantly higher in robotic sites (77.3% for robotic sites vs. 65.9% for non-robotic sites; RR 1.17, 95% CI 1.12-1.23, p<0.0001; aRR 1.12, 95% CI 1.08-1.17, p<0.0001). Patients receiving partial nephrectomy at sites with robotic access were more likely to receive a minimally invasive approach compared to patients at non-robotic sites (61.4% vs. 50.9%, RR 1.21, 95% CI 1.12-1.30; aRR 1.16, 95% CI 1.08-1.25, p<0.0001). The proportion of patients managed by active surveillance was not significantly different between robotic (405, 16.9%) and non-robotic (258, 14.7%) sites (RR 1.15, 95% CI 0.99-1.32; aRR 0.97, 95% CI 0.84-1.12). CONCLUSIONS: Access to robotic kidney surgery was associated with increased use of partial nephrectomy and minimally invasive partial nephrectomy. Use of active surveillance was similar at robotic and non-robotic institutions. Limitations of this study include lack of data on perioperative complications and cancer recurrence.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".