Robotic surgery improves transfusion rate and perioperative outcomes using a broad implementation process and multiple surgeon learning curves
Bibliographic record
Abstract
INTRODUCTION: Data from a randomized trial suggest transfusion rates are similar for robotic and open prostatectomy. The objective of this study was to compare perioperative outcomes of robotic and open prostatectomy at a Canadian academic centre. METHODS: A retrospective review of all prostatectomies performed by all surgeons at The Ottawa Hospital between 2009 and 2016 was completed. Cases and outcomes were identified using an administrative data warehouse. Extracted data included patient factors (age, body mass index, American Society of Anesthesiologists score, Elixhauser comorbidity score), operative factors (length of operation, surgical approach, anesthesia type), and perioperative outcomes (length of recovery room and hospital stay, transfusion rate, hospital cost). Baseline characteristics and outcomes were compared between robotic and open surgical approaches. The primary outcome was transfusion during the index admission. RESULTS: A total of 1606 prostatectomies were performed by 12 surgeons during the study period (840 robotic, 766 open). The rate of transfusion was lower in patients undergoing robotic compared to open surgery (0.6% vs. 11.2%; p<0.001). The robotic prostatectomy cohort had a shorter length of stay in the recovery room (155.7 vs. 231.1 minutes; p<0.001) and shorter length of hospital admission (1.4 vs. 2.8 days; p<0.001). Hospital costs per case were approximately $800 more for robotic prostatectomy ($11 475 vs. $10 656; p<0.001). CONCLUSIONS: This hospital-wide analysis revealed that robotic prostatectomy is associated with a lower transfusion rate compared to the open approach. Further studies emphasizing patient-reported outcomes are needed.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".