Adverse In-Hospital Outcomes Following Robot-Assisted vs. Open Radical Prostatectomy in Quadragenarians
Bibliographic record
Abstract
Background/Objectives: Adverse in-hospital outcomes at radical prostatectomy have not been specifically addressed in young patients aged 40–49 years (quadragenarians). Additionally, no comparison between robot-assisted (RARP) vs. open radical prostatectomy (ORP) has been reported in this population. Methods: Descriptive analyses, propensity score matching (PSM), and multivariable logistic/Poisson regression models addressed quadragenarians undergoing RARP or ORP within the National Inpatient Sample (2009–2019). Results: Of 5426 quadragenarians, 4083 (75.2%) and 1343 (24.8%) underwent RARP and ORP, respectively. The proportion of RARP increased from 68.1 to 84.5% (2009–2019, EAPC: +2.8%, p < 0.001). Adverse in-hospital outcomes after RARP were invariably lower than those after ORP. Specifically, the rates of overall complications (7.8 vs. 13.4%, Δ −5.6%, multivariable odds ratio (OR): 0.54), blood transfusions (1.2 vs. 6.3%, Δ −5.1%, OR: 0.21), and length of stay (LOS) > 2 days (10.6 vs. 28.7%, Δ −18.1%, OR: 0.32) were lower after RARP than after ORP (all p < 0.001). After additional one-to-one PSM between ORP and RARP patients, virtually the same results were reported (overall complications: 7.0 vs. 13.4%, Δ −6.4%, OR: 0.49; blood transfusion rates: 1.5 vs. 6.3%, Δ −4.8%, OR: 0.23; LOS > 2 days: 10.9 vs. 28.7%, Δ −17.8%, OR: 0.30). Conversely, RARP use resulted in higher total hospital charges (USD 43,690 vs. 36,840, Δ USD +6850, IRR: 1.18; p < 0.001). Conclusions: Quadragenarians exhibited a more favorable adverse in-hospital outcome profile after RARP vs. ORP. These advantages are offset by a small, albeit significant, increase in total hospital charges.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".