Robotic prostatectomy is associated with increased patient travel and treatment delay
Bibliographic record
Abstract
INTRODUCTION: New technologies may limit access to treatment. We investigated radical prostatectomy (RP) access over time since robotic introduction and the impact of robotic use on RP access relative to other approaches in the modern era. METHODS: Using the National Cancer Data Base, RPs performed during the eras of early (2004-2005) and late (2010-2011) robotic dissemination were identified. The primary endpoints, patient travel distance and treatment delay, were compared by era, and for 2010-2011, by surgical approach. Analyses included multivariable and multinomial logistic regression. RESULTS: 138 476 cases were identified, 32% from 2004-2005 and 68% from 2010-2011. In 2010-2011, 74%, 21%, and 4.3% of RPs were robotic, open, and laparoscopic, respectively. Treatment in 2010-2011 and robotic approach were independently associated with increased patient travel distance and longer treatment delay (p<0.001). Men treated robotically had 1.1-1.2 times higher odds of traveling medium-to-long-range distances and 1.2-1.3 higher odds of delays 90 days or greater compared to those treated open (p<0.001). Laparoscopic approach was associated with increased patient travel and treatment delay, but to a lesser extent than the robotic approach (p<0.001). In high-risk patients, treatment delays remained significantly longer for minimally invasive approaches (p<0.001). Other factors associated with the robotic approach included referral from an outside facility, treatment at an academic or high-volume hospital, higher income, and private insurance. Potential limitations include the retrospective observational design and lack of external validation of the primary outcomes. CONCLUSIONS: The robotic approach is associated with increased travel burden and treatment delay, potentially limiting access to surgical care.
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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 | 0.000 | 0.004 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".