Adenomyosis: US Features with Histologic Correlation in an in Vitro Study
Bibliographic record
Abstract
PURPOSE: To evaluate the accuracy of ultrasonographic (US) features of adenomyosis by correlating them with histologic findings and to assess inter- and intraobserver agreement. MATERIALS AND METHODS: US was performed and videotaped in 102 consecutive hysterectomy specimens in a water bath. Videotapes were reviewed initially by two independent radiologists blinded to the clinical and histologic findings and after 1 month by one of the two; US and histologic findings were correlated. Features evaluated included diffuse abnormal echotexture of myometrium, subendometrial myometrial cysts, subendometrial echogenic nodules, subendometrial echogenic linear striations, nodular endometrial-myometrial junction, poor definition of the endometrial-myometrial junction, asymmetric thickness of the anteroposterior wall of the myometrium, and globular configuration. RESULTS: The prevalence of adenomyosis in this cohort was 29.4% (30 of 102 specimens). The mean sensitivity, specificity, negative predictive value, positive predictive value (PPV), and accuracy for the diagnosis of adenomyosis for the three reviews were 81%, 71%, 90%, 54%, and 74%, respectively. All findings evaluated, except for nodular endometrial-myometrial junction, were significantly more common in uteri with adenomyosis (P <.05). Heterogeneous myometrium reached borderline significance (P =.05). The specificities and PPVs of subendometrial striations, subendometrial echogenic nodules, and asymmetric myometrial thickness were significantly higher than those of other features (P <.05). The interobserver agreement was moderate (kappa = 0.48), and the intraobserver agreement was good (kappa = 0. 67) for the three reviews. CONCLUSION: The presence of subendometrial linear striations, subendometrial echogenic nodules, or asymmetric myometrial thickness improves the specificity and PPV of US in diagnosing adenomyosis.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".