The Management of Vaginal Vault Dehiscence After Laparoscopic Hysterectomy: A Surgical Techniques Video
Bibliographic record
Abstract
Objective:This article reviews the epidemiology, diagnosis, and management of vaginal vault dehiscence, and illustrates a 5-step surgical approach to laparoscopic vault repair. Methods:At a tertiary care center, surgical footage was obtained from the case of a 34-year-old female who presented 8 weeks after a total laparoscopic hysterectomy with a complete vault dehiscence. Results:Vaginal vault dehiscence complicates 0.64% to 1.35% of laparoscopic hysterectomies, and can be categorized as complete cuff dehiscence, partial cuff dehiscence; or partial thickness. Protective factors include using barbed sutures, compared to nonbarbed sutures, and laparoscopic closure, compared to vaginal closure. Smoking and low body mass index have been associated with an increased risk of dehiscence. The surgical approach to a laparoscopic repair of cuff dehiscence can be standardized in 5 reproducible steps: (1) abdominal survey; (2) bladder and/or rectal dissection; (3) vault debridement; (4) vault closure; and (5) cystoscopy. Conclusion:While uncommon, vaginal vault dehiscence is a serious complication following laparoscopic hysterectomy that requires prompt evaluation and surgical repair. (J GYNECOL SURG 39:300)
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".