A Prospective Study on the Application of Endometrial Cytology Examination in the Screening of Endometrial Cancer
Bibliographic record
Abstract
Background: Endometrial cancer is one of the most common gynecology malignancies. But there is still lack of an accurate, reliable, convenient, easy, economical and practical method for early detection of endometrial cancer and precancerous lesions. The aim of this study is to evaluate the specimen quality and diagnostic accuracy of endometrial cytology examination in the screening of endometrial cancer. Methods: 95 patients with abnormal uterine bleeding or vaginal ultrasound examination results indicating intrauterine abnormalities and needing endometrial examination were investigated, and specimens were collected using endometrial sampling device for cytology (ESDC) and sliced using thinprep cytology test (TCT), meanwhile, hysteroscopy auxiliary diagnostic curettage and histopathological examination were performed. Results: The satisfaction rate was 100% for the specimens collected using ESDC and 97.9% for those using diagnostic curettage. The difference in the satisfaction rate of collecting specimens was statistically significant between the two methods, and the satisfaction rate of collecting specimen using endometrial cytology was superior to that using diagnostic curettage. Taking diagnostic curettage histopathologic results as "gold standard", the sensitivity of endometrial cytology was 63.6%, specificity 93.6%, positive predictive value 70% and negative predictive value 95.2% in the screening of endometrial cancer. Conclusion: Endometrial cytology can be used as a reliable, safe and simple method in the screening of endometrial cancer.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".