Office hysteroscopic metroplasty: three "diagnostic criteria" to differentiate \nbetween septate and bicornuate uteri
Bibliographic record
Abstract
STUDY OBJECTIVE: To evaluate the benefits of adopting 3 simple “diagnostic criteria” in the \ndifferential diagnosis between septate and bicornuate uteri, and the relative treatment by hysteroscopy in an office setting. \nDESIGN: Prospective clinical study (Canadian Task Force classification III). SETTING: University-affiliated hospital. PATIENTS: Two hundred-sixty patients with a hysteroscopic diagnosis of a double uterine cavity \nwere enrolled. INTERVENTIONS:\tOffice hysteroscopic metroplasty was performed without analgesia or anesthesia \nusing 5F scissors. MEASUREMENTS AND MAIN RESULTS:\tThe presence of vascularized tissue, sensitive innervation, and \nthe appearance of the tissue at the incision of a supposed septum during an office hysteroscopic procedure were the criteria used to differentiate a septate from a bicornuate uterus. In 93.1% of the cases, office hysteroscopic metroplasty was successfully performed during the same diagnostic procedure. In 15 of 18 patients scheduled for laparoscopic control of the uterine anatomy, the suspicion of a bicornuate uterus was confirmed. Hysteroscopic follow-up at 3 months showed a regular uterine cavity with a fundal notch less than 1 cm. \nCONCLUSION: The study demonstrates the possibility of obtaining complete, safe removal of uterine septae in most cases by office hysteroscopy confirmation, using mechanical instruments, in an office setting. This was achieved by relating the diagnosis and treatment to simple anatomic and physiologic diagnostic criteria.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".