Surgeon-specific factors affecting treatment decisions among Canadian urologists in the management of pT1a renal tumours
Bibliographic record
Abstract
INTRODUCTION: The ubiquitous use of diagnostic imaging has resulted in an increased incidental detection of small renal masses (SRM). Patient- and tumour-related factors affect treatment decisions greatly; however, with multiple treatment options available, surgeon-specific characteristics and biases may also influence treatment recommendations. We determine the impact of surgeon-specific factors on treatment decisions in the management of SRM in Canada. METHODS: An online survey study was conducted among Canadian urologists currently registered with the Canadian Urological Association. The questionnaire collected demographic information and recommended treatments for 6 SRM index cases involving theoretical patients of various ages (51-80 years) and comorbidities. RESULTS: A total of 110 urologists responded (17% response rate) to the survey. Of these, 18% were over 65 years old and 45% were from academic centres. With increasing patient age and comorbidity, active surveillance and thermal ablative therapies were more the recommended treatment. Laparoscopic/robotic surgery was more commonly recommended by academic urologists and those under 65. Recommending surgery (radical nephrectomy or partial nephrectomy) for both elderly (about 80 years old) index patients correlated with surgeon age (surgeons over 65, p < 0.001), surgeons with no oncologic fellowship training (p = 0.021), surgeons with a non-academic practice (p = 0.003), surgeons with a personal history of cancer (p = 0.038) and surgeons with a family history of cancer death in the last 10 years (p = 0.022). CONCLUSIONS: There are various factors that influence the management options offered to patients with SRMs. Our results suggest that surgeon age, personal history of cancer, practice-type and other surgeon-specific variables may affect treatments offered among urologists across Canada.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".