Effect of Bilateral Salpingectomy on Total Hysterectomy Cost: A National Inpatient Sample Analysis [18D]
Bibliographic record
Abstract
INTRODUCTION: Increasing prevalence of bilateral salpingectomy (BS) at time of hysterectomy has led to concern of increasing healthcare cost. A Canadian study found that salpingectomy at time of hysterectomy was associated with a lower lifetime cost than hysterectomy alone. Given the different healthcare system in Canada these findings may not apply in the United States. Our objective was to determine the cost associated with BS at time of hysterectomy based on USA healthcare data. METHODS: The 2013 National Inpatient Sample (NIS) was used to identify women over age 18 years who underwent hysterectomy with BS for benign indications. The primary outcome was higher cost defined as cost above the median. Stepwise multivariate regression analysis was used to evaluate the effect of co-variables on cost of hysterectomy. Statistical analysis was performed using JMP 10 (SAS, Carey NC). RESULTS: 18,717 hysterectomies with BS were identified, of these 17.1% were laparoscopic, 11.2% were robotic, 58% were abdominal and 14.2% were vaginal. For all types of hysterectomy, BS was associated with a median increase in cost of $1193 ($685-$1701). For vaginal hysterectomy, the median increase in cost was $2080 ($1733-$2427). Robotic, abdominal and laparoscopic hysterectomy were all associated with an increase of less than $75 if BS was performed. Only vaginal hysterectomy was associated with a statistically significant increase in risk of hysterectomy cost being above the median (aOR = 1.57, p=0.01). CONCLUSION: Bilateral salpingectomy at time of vaginal hysterectomy is more likely to be associated with an increase in cost of hysterectomy.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".