Evaluating trends in radical prostatectomy approach and 30-day complication rate in Ontario from 2010–2019
Bibliographic record
Abstract
INTRODUCTION: Radical prostatectomy (RP) for prostate cancer has changed over the years with the advent of minimally invasive (MIRP) approaches, which includes robotic-assisted RP (RARP). The MIRP approaches have been shown to reduce complication rate, but there remain barriers to adoption. The objective of this study was to quantitatively describe the trend in the RP approach in Ontario, and to assess the trend in complication rates. METHODS: We conducted a population-based, retrospective cohort study including all men who underwent RP for prostate cancer in Ontario from 2010-2019. We used administrative data from Ontario's health databases to gather surgical outcome data. Our primary outcomes were the annualized frequency of RP by surgical approach and annualized 30-day composite complication rate. RESULTS: In total, 22 118 patients were included in the analysis over the study period. There was a trend away from retropubic (RRP) frequency over the study period (80.3% of cases in fiscal year [FY] 2010 to 55.6% in FY 2018) and towards RARP approach (6.8% of cases in FY 2010 and 36.7% in FY 2018). The most common complication was blood transfusion at 6.26%, which saw a downtrend over the study period (7.96% FY 2010, 3.47% FY 2018). The odds ratio for 30-day complication for open RP compared to MIRP was 1.74 (95% confidence interval 1.57-1.92, p<0.001). CONCLUSIONS: In Ontario, there has been a steady shift away from RRP and towards RARP. Minimally invasive approaches portend a significantly lower complication rate, likely driven by a lower blood transfusion 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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".