Differences in Patient Characteristics among Men Choosing Open or Robot-Assisted Radical Prostatectomy in Contemporary Practice - Analysis of Surveillance, Epidemiology, and End Results Database
Why this work is in the frame
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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.
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.
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 | 0.006 |
| 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 it