Impact of the introduction of a robotic training programme on prostate cancer stage migration at a single tertiary referral centre
Bibliographic record
Abstract
OBJECTIVE: To evaluate the trend in robot-assisted radical prostatectomy (RARP) and open retropubic radical prostatectomy (RRP) use over time and to compare preoperative and pathological characteristics of patients treated with RARP or RRP at a single centre. PATIENTS AND METHODS: Between 2006 and 2010, 2511 consecutive patients treated with RP, with or without pelvic lymph node dissection (PLND), for prostate cancer (PCa) at a single tertiary care centre were analysed. Baseline patient characteristics and PCa risk distribution were compared according to treatment type (RRP vs RARP) in the overall population, as well as in three surgeons' initial 50 RARP and three surgeons' initial 50 RRP cases (n = 300). We used a chi-squared trend test to evaluate the differences in treatment type administration over time according to PCa characteristics. Logistic regression analyses focused on the prediction of PLND and adjuvant radiotherapy (RT) use. RESULTS: Overall, 1873 (74.6%) and 638 (25.4%) patients underwent RRP and RARP, respectively. Men treated with RARP were younger (mean age: 62 vs 65 years), less obese (mean BMI: 24.8 vs 26.4 kg/m(2) ), healthier (Charlson comorbidity index = 0: 68.7 vs 53.3%) and more likely to harbour clinical low-risk PCa (51 vs 30%) than their RRP counterparts (all P < 0.001). Similar findings were observed in sub-analyses focusing on six surgeons' 50 initial patients (all P ≤ 0.02). A significant increase in the rate of patients with low-risk PCa treated with RARP vs RRP was reported over time (5 vs 95% and 66 vs 34% in 2006 and 2010, respectively). Conversely, 76% of patients with high risk PCa were still treated with RRP in 2010. Patients treated with RARP were less likely to receive PLND at RP and adjuvant RT (all P ≤ 0.01), even after adjusting for clinical and PCa characteristics. CONCLUSIONS: The introduction of a robotic training programme at a high volume centre led to significant patient selection in terms of clinical and PCa characteristics. When both RRP and RARP facilities are available within the same centre, patients with the most favourable clinical and cancer profile are selected to undergo RARP. Use of RARP negatively influenced the rates and the extent of PLND as well as the use of adjuvant RT after surgery. Thus, baseline patient selection, surgical and treatment biases make any comparisons of RARP with RRP problematic.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".