Ten-year experience of robot-assisted radical prostatectomy: the road from cherry-picking to standard procedure
Bibliographic record
Abstract
BACKGROUND: Patients treated with robot-assisted radical prostatectomy (RARP) are frequently selected according to more favorable characteristics. Such patient selection might decrease according to increasing experience. METHODS: We relied on the Martini Clinic Prostate Cancer Center database and focused on patients treated with RARP between 2004 and 2013. Differences in clinical, pathological and surgical characteristics at RARP over time (2004-2010, 2011-2012 and 2013) were assessed. RESULTS: Overall, 1783 RARP patients were identified. Of those, 407 (22.8%), 764 (42.8%) and 612 (34.3%) were treated between 2004 and 2010, in 2011-2012 and in 2013, respectively. Unfavorable characteristics rate, such as biopsy Gleason Score ≥4+4 (8 vs. 9 vs. 15%, P<0.001), D'Amico high-risk (12 vs. 14 vs. 19%, P=0.001) and pathological Gleason score ≥4+4 (3 vs. 4 vs. 6%, P<0.001) increased over time. Pelvic lymph node dissection (PLND) was more frequently performed over time (62 vs. 83 vs. 84%, P<0.001), especially in D'Amico intermediate or high-risk patients (82 vs. 94 vs. 96%, P<0.001). Lymph node yield increased over time in overall (7 vs. 9 vs. 13, P<0.001), D'Amico intermediate (6 vs. 9 vs. 12, P<0.001) and D'Amico high-risk patients (9 vs. 12 vs. 18, P<0.001). No differences in surgical margin (P=0.7) and nerve sparing rates (P=0.09) were found. CONCLUSIONS: A clear trend towards more unfavorable tumor characteristics over time was recorded. Additionally, the rates and extent of PLND increased with increasing experience. RAR P does not represent a barrier to PLND at our institution.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".