Bibliographic record
Abstract
T he current study updates the data on trends in pelvic organ prolapse surgery in the United States, comparing robotic-assisted vaginal vault suspension (RAVVS) to open vaginal vault suspension (OVVS). 1 The authors look at outcomes and utilization in the Nationwide Inpatient Sample (NIS) from 2009 to 2010.The analysis shows an increase in utilization of RAVVS over time, lower blood loss, higher intraoperative complications, and higher charges, but equivalent overall postoperative complications.Large population-based studies, in my mind, do more to raise questions than to answer them.However, there is great value in raising the questions.Any assessment of robotic outcomes needs to take into account the stage of familiarity of the surgeon.The fact that utilization was still increasing during the 1-year sample implies that RAVVS was still being adopted by some of the surgeons sampled.The advantages of the NIS include large sample size and a broad representation of practice type.However, it is not possible to distinguish results from high volume surgeons later on the learning curve from lower volume surgeons or those newer to the technology.The annual caseload at the centres performing RAVVS was higher, but this was not broken down by surgeon -the higher volume centre might have been more likely to purchase a robot or to hire an additional newer surgeon.The authors comment that the higher intraoperative complication rate may be attributable to the learning curve.The perioperative complications captured were injury to organ nerve or vessel, transfusion, death, prolonged length of stay, elevated hospital charges, cardiac, wound, vascular, genitourinary, neurological, infectious, and miscellaneous complications, and death.More
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".