Comparative Effectiveness of Robot-Assisted and Open Radical Prostatectomy in the Postdissemination Era
Bibliographic record
Abstract
PURPOSE: Given the lack of randomized trials comparing robot-assisted radical prostatectomy (RARP) and open radical prostatectomy (ORP), we sought to re-examine the outcomes of these techniques using a cohort of patients treated in the postdissemination era. PATIENTS AND METHODS: Overall, data from 5,915 patients with prostate cancer treated with RARP or ORP within the SEER-Medicare linked database diagnosed between October 2008 and December 2009 were abstracted. Postoperative complications, blood transfusions, prolonged length of stay (pLOS), readmission, additional cancer therapies, and costs of care within the first year after surgery were compared between the two surgical approaches. To decrease the effect of unmeasured confounders, instrumental variable analysis was performed. Multivariable logistic regression analyses were then performed. RESULTS: Overall, 2,439 patients (41.2%) and 3,476 patients (58.8%) underwent ORP and RARP, respectively. In multivariable analyses, patients undergoing RARP had similar odds of overall complications, readmission, and additional cancer therapies compared with patients undergoing ORP. However, RARP was associated with a higher probability of experiencing 30- and 90-day genitourinary and miscellaneous medical complications (all P ≤ .02). Additionally, RARP led to a lower risk of experiencing blood transfusion and of having a pLOS (all P < .001). Finally, first-year reimbursements were greater for patients undergoing RARP compared with ORP (P < .001). CONCLUSION: RARP and ORP have comparable rates of complications and additional cancer therapies, even in the postdissemination era. Although RARP was associated with lower risk of blood transfusions and a slightly shorter length of stay, these benefits do not translate to a decrease in expenditures.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".