Incorporating VR-RENDER Fusion Software in Robot-Assisted Partial Prostatectomy: The First Case Report
Bibliographic record
Abstract
Currently, the active surveillance of men with favorable intermediate-risk localized prostate cancer (PCa) is a longstanding controversy, in terms of their oncological outcomes, and radical prostatectomy would constitute a similar concern of overtreatment, regarding its functional outcomes. Thus, focal therapy could be considered in men belonging to favorable intermediate-risk group. Among all focal therapies, high-intensity focused ultrasound (HIFU) was the most studied methodology in clinical trials. Although HIFU provided better functional outcomes than radical prostatecomy, the oncological outcomes were inferior in men with intermediate-risk localized PCa. Two articles have been published discussing the feasibility and clinical outcomes of robot-assisted partial prostatectomy (RAPP), and both the functional and oncological outcomes were superior than those with HIFU. However, the rate of positive surgical margins (PSMs) was reported as high in the literature. Here, we present a case of favorable intermediate-risk localized PCa with an isolated tumor at the anterior apex. After reconstructing a personal three-dimensional (3D) image, we utilized it in a 3D image-guided precise excise, followed by intraoperative frozen specimen review. We found that this method may present a resolution to the high PSM rate documented in the current literature regarding RAPP. This method merits further study with a well-designed prospective study.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".