Best predictors of grayscale ultrasound combined with color doppler in the diagnosis of retained products of conception
Bibliographic record
Abstract
OBJECTIVES: To determine the best predictors of the presence of retained products of conception (RPOC) on grayscale and color Doppler transvaginal sonographic examination. METHODS: This was a retrospective study of 91 consecutive patients who underwent transvaginal sonography (TVS) with color Doppler to evaluate for the presence of RPOC. The images of TVS studies were reviewed by two radiologists in consensus blinded to the final outcome. Data on a number of variables including endometrial measurable mass and focal increased color vascularity were collected as predictors of RPOC. The patients' ages ranged from 17 to 48 years (mean, 31.8 ± 6.8) and gestational age from 5 to 24 weeks (mean, 9.2 ± 3.8). Thirty-six were confirmed as RPOC by dilatation and curettage (D&C) and pathology. Fifty-five were considered negative, 9 based on D&C results and 46 on clinical grounds. RESULTS: Sensitivity, specificity, negative- and positive-predictive and accuracy values were 81% (CI: 68%-94%), 71% (CI: 59%-83%), 85% (CI: 74%-95%), 64% (CI: 50%-78%), and 75% (CI: 66%-84%) to detect RPOC when a mass was present. The corresponding numbers for the presence of focal color vascularity were 94% (CI: 87%-100%) (p = 0.07), 67% (CI: 55%-80%) (p > 0.05), 95% (CI: 88%-100%) (p = 0.1), 65% (CI: 52%-78%) (p > 0.05), and 78% (CI: 70%-87%) (p > 0.05). Of the patients with confirmed RPOC on pathology, five had focal increased vascularity and no massand none had a mass without focal increased vascularity. CONCLUSION: An area of focal increased vascularity with or without a mass is the best predictor of the presence of RPOC.
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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 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".