A population based study of surgeon characteristics associated with the uptake of contemporary techniques in renal surgery
Bibliographic record
Abstract
INTRODUCTION: We have witnessed the slow uptake of many contemporary techniques in the surgical management of renal tumours. We sought to evaluate surgeon-level characteristics associated with the uptake of laparoscopy, partial nephrectomy (PN) and adrenal-sparing approaches in surgically managing these tumours. METHODS: Using the Ontario Cancer Registry, we identified surgeons treating renal cell carcinoma (RCC) in the province of Ontario, Canada between 2002 and 2004. We then classified individuals within this cohort as either high or low utilizers of laparoscopy, PN or adrenal-sparing approaches. Further variables analyzed included academic status, surgeon graduation year and surgical volume status. We then used univariable and multivariable logistic regression models to assess predictors of uptake. RESULTS: We evaluated a total of 108 surgeons for their uptake of both laparoscopy and adrenal-sparing approaches and 94 surgeons for their uptake of PN. We identified 32 surgeons (30%) as high users of laparoscopy. Predictors of uptake of laparoscopy included graduation year after 1990 (odds ratio [OR] 4.81, confidence interval [CI] 1.57-14.8) and high-surgeon volume (OR 4.33, CI 1.60-10.4). We identified 41 surgeons (44%) as high users of PN. The only predictor of uptake of PN was academic status (OR 5.83, CI 1.96-17.3). We identified 69 surgeons (65%) as high users of adrenal-sparing approaches, but did not identify any significant predictors for uptake in this group. DISCUSSION: We identify unique factors contributing to the uptake of distinct surgical techniques in the management of RCC. This information sheds lights on the underlying mechanisms and helps us understand how to further encourage the dissemination of these practices.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".