Contemporary National Assessment of Robot-Assisted Surgery Rates and Total Hospital Charges for Major Surgical Uro-Oncological Procedures in the United States
Bibliographic record
Abstract
Background:The role of robot assistance is increasingly gaining importance among all major surgical uro-oncological procedures (MSUPs). However, contemporary analyses showed that total hospital charges (THCGs) related to robot-assisted procedures exceed those of open procedures. Based on increasing familiarity with robot-assisted surgery, we postulated that THCGs may have decreased over the past half-decade. Thus, we tested contemporary trends and THCGs related to robot-assisted vs nonrobot-assisted MSUPs. Materials and Methods:Within the National Inpatient Sample database (2009–2015), we identified patients who underwent robot-assisted vs nonrobot-assisted (open or laparoscopic) MSUPs, which included radical prostatectomy (RP), radical nephrectomy (RN), partial nephrectomy (PN), and radical cystectomy (RC). Rates of robot-assisted MSUPs were evaluated using estimated annual percentage changes (EAPCs) analyses. The t-test was used to examine statistically significant differences between mean THCGs according to either robot-assisted or nonrobot-assisted approach. Finally, linear regression analyses were tested for annual variation in the mean THCGs. Results:Of 128,367 MSUPs, 47.7% were robot-assisted. Overall, robot-assisted surgery rates among MSUPs increased from 40.3% to 57.6% (EAPC: +6.3%, p < 0.001) between 2009 and 2015. The mean THCGs for robot-assisted RP, RN, PN, and RC were $13,799, $18,789, $16,574, and $33,575, respectively. The observed mean THCGs differences between robot-assisted and nonrobot-assisted MSUPs were +$1594, +$1592, and +$1829 for RP, RN, and RC, respectively (all p < 0.05). Conversely, no statistically significant difference in the mean THCGs was reported between robot-assisted and nonrobot-assisted PN (+$367, p > 0.05). Finally, the annual observed mean THCGs linearly decreased for all robot-assisted MSUPs during the study period. Conclusions:Rates of robot-assisted MSUPs exponentially increased between 2009 and 2015. Although the mean THCGs decreased in a significant manner during the study period for all MSUPs, THCGs of robot-assisted RP, RN, and RC still exceed those of their respective nonrobot-assisted counterparts. Conversely, no differences in the mean THCGs were reported between robot-assisted vs nonrobot-assisted PN.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".