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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".