Predictors of deviation in neurovascular bundle preservation during robotic prostatectomy.
Bibliographic record
Abstract
INTRODUCTION: Neurovascular bundle (NVB) preservation during robot-assisted radical prostatectomy (RARP) directly affects patient functional outcomes. Despite careful surgical planning, many NVB preservation techniques are changed intraoperatively from their preoperative plan. Our objective was to identify risk factors predicting intraoperative change in NVB preservation technique during RARP. MATERIALS AND METHODS: Prospective data from 578 RARPs performed by a single surgeon between 2010 and 2017 at a tertiary care center. Side-specific NVB preservation technique was planned preoperatively. Surgical techniques were either complete nerve sparing (CNS), or incomplete nerve sparing (INS). Variables included age, tumor grade, prostate volume, number of lifetime biopsies, history of post-biopsy sepsis, and laterality. Variables were modeled in multivariable logistic regressions as potential predictors of deviation in surgical technique. Functional and oncological outcomes were also assessed. RESULTS: A total of 46.9% of cases underwent some intraoperative change in NVB preservation from their preoperative plan. A total of 37.7% of 880 prostate sides planned for CNS underwent unplanned INS. Older age, Gleason ≥ 3+4, post-biopsy sepsis, prostate volume, and left-sided dissections were significantly associated with unplanned INS. Number of lifetime biopsies was not a predictor of unplanned INS. Patients with an intraoperative change to INS had poorer potency and continence. Study limitations included the retrospective nature of analysis and lack of pathological assessment of NVB preservation. CONCLUSIONS: Age, Gleason ≥ 3+4, post-biopsy sepsis, prostate volume, and laterality were significant predictors of unplanned INS during RARP, which should guide patient counseling when discussing risks and functional outcomes. The number of lifetime biopsies did not predict unplanned INS, a valuable finding for patients on active surveillance. Our findings highlight the importance of careful preoperative planning and novel adjuncts such as multiparametric MRI.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".