The Value of Chromohysteroscopy in the Assessment of Postmenopausal Vaginal Bleeding
Bibliographic record
Abstract
Background: To assess the endometrial changes in postmenopausal patients with vaginal bleeding, using chromohysteroscopy and histopathology, concluding consequently the value of chromohysteroscopy in investigating those patients. Methods: We included a group of 50 postmenopausal women, presenting with vaginal bleeding, to the outpatient clinic of Cairo University hospital. Transvaginal 2D ultrasonography was performed, followed by hysteroscopy, with chromohysteroscopy performed. Hysteroscopic guided biopsies were obtained from stained areas, then formal curettage done to all uterine walls. Results: Chromohysteroscopy patterns were either no staining (14%), staining of focal area(s) (76%) or staining of the whole endometrium (10%). Statistical significance was detected between endometrial thickness and chromohysteroscopy. We found a sensitivity 93.75%, specificity 27.77%, PPV 69.76%, and NPV 71.42% of chromohysteroscopy for the pathology obtained. Conclusion: Chromohysteroscopy improves the efficacy of hysteroscopy in targeting endometrial biopsy. It is though labor intensive and yet not standardized. Studies involving different dyes and larger number of participants are warranted. Inter-observer variability is equally important to help standardizing the procedure. J Clin Gynecol Obstet. 2014;3(1):35-41 doi: http://dx.doi.org/10.14740/jcgo209w
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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 | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".