Volume-Outcome Relationship in Intra-abdominal Robotic-Assisted surgery. A Systematic Review
Bibliographic record
Abstract
Abstract As robotic assisted surgery (RAS) expands to smaller centres, platforms are shared between specialities. Healthcare providers must consider case volume and mix required to maintain quality and cost-effectiveness. This can be informed, in-part, by the volume-outcome relationship. We perform a systematic review to describe the volume-outcome relationship in intra-abdominal robotic assisted surgery to report on suggested minimum volumes standards. A literature search of Medline, NICE Evidence Search, Health Technology Assessment Database and Cochrane Library using the terms: “robot*”, “surgery”, “volume” and “outcome” was performed. The included procedures were gynaecological: hysterectomy, urological: partial and radical nephrectomy, cystectomy, prostatectomy, and general surgical: colectomy, oesophagectomy. Hospital and surgeon volume measures and all reported outcomes were analysed. 41 studies, including 983149 procedures, met the inclusion criteria. Study quality was assessed using the Newcastle-Ottawa Quality Assessment Scale and the retrieved data was synthesised in a narrative review. Significant volume-outcome relationships were described in relation to key outcome measures, including operative time, complications, positive margins, lymph node yield and cost. Annual surgeon and hospital volume thresholds were described. We concluded that in centres with an annual volume of fewer than 10 cases of a given procedure, having multiple surgeons performing these procedures led to worse outcomes and, therefore, opportunities should be sought to perform other complimentary robotic procedures or undertake joint cases.
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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 | 0.011 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".