Analyzing the influence of expanding multispecialty adoption of robotic surgery on robotic urologic care
Bibliographic record
Abstract
INTRODUCTION: Most robot-assisted surgery (RAS) systems in Canada are donor-funded, with constraints on implementation and access due to significant costs, among other factors. Herein, we evaluated the impact of the growing multispecialty use of RAS on urologic RAS access and outcomes in the past decade. METHODS: We conducted a retrospective review of all RAS performed by different surgical specialties in two high-volume academic hospitals between 2010 and 2019 (prior to the COVID pandemic). The assessed outcomes included the effect of increased robot access over the years on annual robotic-assisted radical prostatectomy (RARP) volumes, surgical waiting times (SWT), and pathologically positive surgical margins (PSM). Data were collected and analyzed from the robotic system and hospital databases. RESULTS: In total, six specialties (urology, gynecology, general, cardiac, thoracic, and otorhinolaryngologic surgery) were included over the study period. RAS access by specialty doubled since 2010 (from three to six). The number of active robotic surgeons tripled from seven surgeons in 2010 to 20 surgeons in 2019. Moreover, there was a significant drop in average case volume, from a peak of 40 cases in 2014 to 25 cases in 2019 (p=0.02). RARP annual case volume followed a similar pattern, reaching a maximum of 166 cases in 2014, then declining to 137 cases in 2019. The mean SWT was substantially increased from 52 days in 2014 to 73 days in 2019; however, PSM rates were not affected by the reduction in surgical volumes (p<0.05). CONCLUSIONS: Over the last decade, RAS access by specialty has increased at two Canadian academic centers due to growing multispecialty use. As there was a fixed, single-robotic system at each of the hospital centers, there was a substantial reduction in the number of RAS performed per surgeon over time, as well as a gradual increase in the SWT. The current low number of available robots and unsustainable funding resources may hinder universal patient access to RAS.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".