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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".