Cardiovascular Disease and Chronic Pulmonary Disease Increase the Risk of Short-Term Major Postoperative Complications after Robotic-Assisted Radical Prostatectomy
Bibliographic record
Abstract
Background and objectives: Certain comorbidities may be associated with a higher risk of complications after robotic-assisted radical prostatectomy. Material and Methods: Relying on a tertiary care database, we identified robotic-assisted radical prostatectomy patients (January 2014–March 2023). Short-term major postoperative complications were defined according to Clavien Dindo as ≥IIIa within 30 days after robotic-assisted radical prostatectomy. Results: Of 1148 patients, the rates of postoperative Clavien Dindo IIIa, Clavien Dindo IIIb, Clavien Dindo IVa, and Clavien Dindo IVb complications were 3.3%, 1.4%, 0.3%, and 0.2%, respectively. Of those, 28 (47%) had lymphoceles, and 8 (13%) had bleeding-associated complications. Patients with cardiovascular disease (8 vs. 4%) or chronic pulmonary disease (13 vs. 5%) were more likely to have complications. In multivariable logistic regression models, cardiovascular disease (odds ratio: 1.78; p = 0.046) and chronic pulmonary disease (odds ratio: 3.29; p = 0.007) remained associated with an increased risk of postoperative complications. Conclusions: Complications after robotic-assisted radical prostatectomy are predominantly manageable without anesthesia. Concomitant cardiovascular disease and chronic pulmonary disease were both associated with a higher risk of postoperative complications.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".