Ultrasound Scoring of Endometrial Pattern for Fast-track Identification or Exclusion of Endometrial Cancer in Women with Postmenopausal Bleeding
Bibliographic record
Abstract
STUDY OBJECTIVE: To evaluate the risk of endometrial cancer (REC) scoring system for the prediction of high and low probability of endometrial cancer (EC) in women with postmenopausal bleeding (PMB). DESIGN: A prospective study (Canadian Task Force classification II-1). SETTING: An academic hospital. PATIENTS: Nine hundred fifty consecutive patients with PMB underwent transvaginal ultrasonography (TVS) and REC scoring between November 2013 and December 2015. INTERVENTIONS: Obstetrics and gynecology residents supervised by trained physicians scored endometrial patterns according to the previously established REC scoring system. The reference standard was endometrial samples, endometrial thickness (ET, 4-4.9 mm), operative hysteroscopy or hysterectomy (ET ≥5 mm), and 1-year follow-up in all patients presenting with ET <4 mm. Diagnostic performance for the prediction of probability of malignancy was assessed using the REC scoring system. MEASUREMENTS AND MAIN RESULTS: The area under the receiver operating characteristic curve of the TVS REC scoring system was 97% (95% confidence interval [CI], 95%-98%) for the prediction of malignancy. In 656 patients with ET ≥4 mm, REC scoring effectively predicted a high probability of malignancy with sensitivity (95% confidence interval) of 92% (95% CI, 87%-95%) and specificity of 94% (95% CI, 91%-96%). An REC score of 0 was present in 206 (32%) patients with ET ≥4 mm and was associated with a low negative likelihood ratio of 0.026 for EC. There were only 7 patients with EC/atypical hyperplasia among these 206 patients. CONCLUSION: The REC scoring system identified or ruled out most ECs, clearly showing that more specific image analysis at first-line TVS can accelerate the diagnosis of EC in patients with PMB and may allow for improved selection of second-line strategies in patients with ET ≥4 mm.
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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 | 0.009 |
| 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) | 0.001 | 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".