Postoperative complications of contemporary open and robot‐assisted laparoscopic radical prostatectomy using standardised reporting systems
Bibliographic record
Abstract
OBJECTIVES: To analyse time trends and contemporary rates of postoperative complications after radical prostatectomy (RP) and to compare the complication profile of open RP (ORP) and robot-assisted laparoscopic RP (RALP) using standardised reporting systems. PATIENTS AND METHODS: Retrospective analysis of 13 924 RP patients in a single institution (2005-2015). Complications were collected during hospital stay and via standardised questionnaire 3 months after, and grouped into eight schemes. Since 2013, the revised Clavien-Dindo classification was used (n = 4 379). Annual incidence rates of different complications were graphically displayed. Multivariable logistic regression analyses compared complications between ORP and RALP after inverse probability of treatment weighting (IPTW). RESULTS: After the introduction of standardised classification systems, complication rates have increased with a contemporary rate of 20.6% (2013-2015). While minor Clavien-Dindo grades represented the majority (I: 10.6%; II: 7.9%), severe complications (Grades IV-V) were rare (<1%). In logistic regression analyses after IPTW, RALP was associated with less blood loss, shorter catheterisation time, and lower risk of Clavien-Dindo Grade II and III complications. CONCLUSION: Our results emphasise the importance of standardised reporting systems for quality control and comparison across approaches or institutions. Contemporary complication rates in a high-volume centre remain low and are most frequently minor Clavien-Dindo grades. RALP had a slightly better complication profile compared to ORP.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".