Ultrasound Diagnostics in Patients with Endometrial Carcinoma
Bibliographic record
Abstract
Introduction: Endometrial carcinoma is diagnosed by histopathological assessment of the sampled endometrium. After establishing the diagnosis the patient needs to be further evaluated in order to establish an optimal treatment. The most important factors that determine the treatment plan include: age, reproduction status, the depth of myometrial invasion, cervical invasion, histopahological type of tumor, histological and nuclear grade. Surgery is the most common treatment. The choice of optimal surgical procedure may include various imaging methods. Aim of the study: Testing the usefulness of applying the ultrasound diagnostics in preoperative evaluation of patients diagnosed with endometrial carcinoma. Method: The prospective study included 61 patients diagnosed with endometrial carcinoma. The ultrasound was used to estimate the presence and depth of invasion of the uterine muscle and cervical inclusion. The obtained parameters were compared to histopathological findings from surgically removed uterus. Results: The sensitivity of the ultrasound method in the estimation of myometrial invasion in the tested sample was 77.59%, specificity was 100.00%, predictive value of the positive test was 79.03%. The sensitivity of the ultrasound method in the estimation of cervical invasion in the tested sample was only 11.11%, specificity was 90.91%, predictive value of the positive test was 33.33%, predictive value of the negative test was 71.43%, whereas total accuracy of the method was 67.74%. Conclusion: Ultrasound diagnostics can be used in the assessment of the depth myometrial invasion but not in the assessment of cervical inclusion.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".