Long-term patient outcomes from the first year of a robotic surgery program using multi-surgeon implementation
Bibliographic record
Abstract
INTRODUCTION: There is concern that surgical quality initially declines during the learning phase of robotic surgery. At our institution, we used a multi-surgeon programmatic approach to the introduction of robotic surgery. The purpose of this study was to evaluate outcomes of patients treated during the first year of our program. METHODS: This is a historical cohort of all radical prostatectomy patients during a one-year period. Baseline, perioperative, and long-term followup data were prospectively and retrospectively collected. Treatment failure was a composite of any postoperative radiation, androgen-deprivation, or prostate-specific antigen (PSA) ≥0.2. RESULTS: During the study period, 225 radical prostatectomy procedures were performed (104 robotic and 121 open). Baseline characteristics were similar between groups (p>0.05). All patients were continent and 74% were potent prior to surgery. Mean estimated blood loss (280 cc vs. 760 cc; p<0.001) and blood transfusion (0% vs. 8.3%; p=0.002) was lower in the robotic cohort. Non-transfusion complications were similar between groups (13% vs. 12%; p=0.7). Mean hospital stay was shorter in the robotic cohort (1.4 vs. 2.5 days). There was no difference in overall positive margin rate (38% vs. 43%; p=0.4) or treatment failure at a median followup of 3.5 years (p=0.4). Robotically treated patients were more often continent (89% vs. 77%; p=0.02) and potent (48% vs. 32%; p=0.02). CONCLUSIONS: Using an inclusive multi-surgeon approach, robotic pros-tatectomy was introduced safely at a Canadian academic institution.
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.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".