Open Versus Robotic Radical Prostatectomy in Obese Men
Bibliographic record
Abstract
OBJECTIVES: Robotic-assisted radical prostatectomy (RARP) has been shown to reduce blood loss, peri-operative complications and length of stay when compared to open radical prostatectomy (ORP). We sought to determine whether the reported benefits of RARP over ORP translate to obese patients. PATIENTS AND METHODS: We utilized the 2009-2010 Nationwide Inpatient Sample to identify all obese men with prostate cancer who underwent ORP and RARP. Our primary outcome was the presence of a peri-operative adverse event (i.e. blood transfusion, complication, prolonged length of stay). We fit multivariable logistic regression models to examine whether RARP in obese patients was independently associated with decreased odds of all three outcomes. RESULTS: We identified 9,108 obese patients who underwent radical prostatectomy. On multivariable analysis, the use of RARP in the obese population was not independently associated with decreased odds of developing a peri-operative complication (OR = 0.81, CI: 0.58-1.13, p = 0.209). RARP was, however, associated with decreased odds of blood transfusion (OR = 0.17, CI: 0.10-0.30, p < 0.001) and prolonged length of stay (OR = 0.28, CI: 0.20-0.40, p < 0.001). CONCLUSION: Our findings suggest that in obese patients, the use of RARP may reduce length of stay and blood transfusions compared to ORP. Both approaches, however, are associated with similar odds of developing a complication.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".