Costs and Outcomes of Abdominal, Vaginal, Laparoscopic and Robotic Hysterectomies
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: To estimate the incidence of operative complications and compare operative cost and overall cost of different methods of benign hysterectomy including abdominal, vaginal, laparoscopic, and robotic techniques. METHODS: We performed a retrospective cohort analysis (Canadian Task Force classification II-2) of all patients who underwent a hysterectomy for benign reasons in 2009 at a single urban academic tertiary care center using the χ(2) test and Student t test. A multivariate regression analysis was also performed for predictors of costs. Cost data were gathered from the hospital's billing system; the remainder of data was extracted from patient's medical records. RESULTS: In 2009, 688 patients underwent a benign hysterectomy; 185 (26.9%) hysterectomies were abdominal, 135 (19.6%) vaginal, 352 (51.5%) laparoscopic, and 14 (2.0%) robotic. The rate of intraoperative complication was 1.7% for abdominal, 0.8% for vaginal, 0.3% for laparoscopic, and 0 for robotic. Mean total patient costs were $43,622 for abdominal, $31,934 for vaginal, $38,312 for laparoscopic, and $49,526 for robotic hysterectomies. Costs were significantly influenced by method of hysterectomy, operative time, and length of stay. CONCLUSION: Though complication rates did not vary significantly among minimally invasive methods of hysterectomy, patient costs were significantly influenced by the method of hysterectomy.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".