Adhesion Barrier Use After Myomectomy and Hysterectomy
Bibliographic record
Abstract
OBJECTIVE: To evaluate use rates and immediate postoperative complications of the use of an adhesion barrier in myomectomy or hysterectomy. METHODS: This was a retrospective cohort study. Using the Healthcare Cost and Utilization Project Nationwide Inpatient Sample, we examined the records of women with primary discharge diagnosis of uterine myoma during the period 2003-2011. We evaluated use rates and complications among 473,788 women treated by myomectomy or hysterectomy. RESULTS: Of 473,788 women treated by myomectomy or hysterectomy, adhesion barrier was used in 3,392 of 62,563 myomectomies (5.4%) and 5,590 of 411,225 hysterectomies (1.4%). The rate of ileus after myomectomy in the nonbarrier group (1,290/59,171 [2%]) was lower than in the barrier group (109/3,392 [3%]; adjusted odds ratio [OR] 1.50 [1.22-1.83]) and similar after hysterectomy (10,329/405,635 [2.5%] compared with 288/5,590 [5%]; adjusted OR 1.97 [1.75-2.23]). Postoperative fever was also higher in the adhesion barrier group after myomectomy (4.4% compared with 2.9%, adjusted OR 1.44 [1.21-1.71]) as well as after hysterectomy (2.5% compared with 1.6%, OR 1.65 [1.40-1.96]). Small bowel obstruction after hysterectomy in the nonbarrier group (804/405,635 [0.2%]) was less frequent than in the barrier group (23/5,590 [0.4%]; OR 1.90 [1.25-2.89]), but not after myomectomy. CONCLUSION: Although the use of adhesion barrier remains very low, the use is associated with a slightly higher incidence of fever and ileus after myomectomy and hysterectomy and with bowel obstruction after 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".