Differences in Patient Characteristics among Men Choosing Open or Robot-Assisted Radical Prostatectomy in Contemporary Practice - Analysis of Surveillance, Epidemiology, and End Results Database
Bibliographic record
Abstract
OBJECTIVE: To examine characteristics of robot-assisted (RARP) and open radical prostatectomy (ORP) patients. PATIENTS AND METHODS: We relied on the Surveillance, Epidemiology, and End Results-Medicare-linked database and focused on prostate cancer patients between 2008 and 2009. In multivariable logistic regression analyses, we predicted RARP. RESULTS: Of 5,915 patients, 3,476 (58.8%) underwent RARP and 2,439 (41.2%) ORP. Patients within intermediate (OR 1.4, p = 0.01) or highest (OR 1.5, p = 0.02) education strata and those treated by surgeons with a high volume (OR 2.2, p < 0.001) were more likely to undergo RARP. Conversely, those residing in rural areas (OR 0.7, p = 0.005) and those with clinical stage T2 or higher (OR 0.7, p = 0.006) were less likely to undergo RARP. Additionally, patients from the Southwest were less likely to undergo RARP (OR 0.4, p < 0.001), but those from the Northern Plains were more likely to undergo RARP (OR 1.4, p = 0.02) than their counterparts from the East. Finally, RARP patients were neither younger nor healthier than ORP patients. CONCLUSIONS: Several patient characteristics such as education, region of residence and population density affect the likelihood of RARP vs. ORP treatment. Similarly, clinical stage and surgeon characteristics also affect the assignment to one or other treatment modality.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".