Local tumour ablation for localized kidney cancer: Practice patterns in Canada
Bibliographic record
Abstract
INTRODUCTION: Local tumour ablation (LTA) is a recommended option for the treatment of localized kidney cancer in nonsurgical candidates. We performed a survey to describe the practice patterns of this procedure in Canada. METHODS: An electronic survey was sent by email to all urologists registered to the Canadian Urological Association (CUA). Urologists were queried about general demographic information, LTA availability at their institution (and reasons for non-availability, if it was the case), as well as the type and context of LTA use. RESULTS: Overall, 103 individual responses were obtained (response rate of 19.5%). Of those, 58 (56.3%) had access to LTA at their institution. Urologists who had access to LTA were more likely to work at an academic institution (69 vs. 16%, p<0.001). Among individuals who did not use LTA, the main reasons were lack of staff, such as radiologists, who can assist and/or perform the procedure (64%); and lack of expertise with the procedure (62%). Among urologists who had access to LTA, percutaneous radiofrequency and cryoablation were the most commonly used (72% and 21%, respectively); however, urologists were rarely involved in those procedures (12%). CONCLUSIONS: In this national survey, we found that a significant proportion of Canadian urologists did not have access to LTA. We also found that when LTA was performed, urologists were rarely involved in the procedures. Those findings represent significant areas for improvement in the access to LTA. The conclusions of this study are limited by the low response rate.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".