Uterine Fundectomy in Patients With Benign Etiology Undergoing Hysterectomy: New Surgical Technique
Bibliographic record
Abstract
BACKGROUND: Hysterectomy is the most common surgical procedure in gynecology, not only in cases of malignancies but also in many benign cases. Many uterine preservation techniques have been introduced as alternatives to hysterectomy. OBJECTIVE: We aimed to propose a new uterine surgical procedure. In this paper, we compare the utility of this new technique to the limitations of current procedures. METHODS: Uterine fundectomy may be considered as a subtotal hysterectomy. In this new technique, the uterine fundus including all pathologic tissue is cut as a reverse trapezoid by monopolar cautery. The upper side of the trapezoid, which includes the whole uterine fundus, is removed, but the fallopian tubes and cornual segment are preserved. A small uterine cavity remains, as well as the endometrial tissue lining it. RESULTS: Patient recruitment for this study began in April 2017 and is expected to end approximately 12 months later. Assessment of the primary outcomes is expected to take place in April 2018. CONCLUSIONS: Uterine preservation is particularly critical in developing new surgical approaches that can lead to a positive impact on patient satisfaction. This protocol outlines the first attempt to prospectively test surgical fundectomy in candidates for hysterectomy for benign indications.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".