Measurement of Lower Uterine Segment Thickness to Detect Uterine Scar Defect: Comparison of Transabdominal and Transvaginal Ultrasound
Bibliographic record
Abstract
OBJECTIVES: Lower uterine segment (LUS) thickness measurement using transabdominal ultrasound (TA-US), transvaginal ultrasound (TV-US), or the combination of both methods can detect scar defect in women with prior cesarean. We aimed to compare the sensitivity of three approaches. METHODS: Women with prior cesarean underwent LUS thickness measurement at 34-38 weeks' gestation. Among those who underwent repeat cesarean before labor, we compared the accuracy of TA-US, TV-US, and the thinner of the two measurements (the "combined measurement") for uterine scar dehiscence using the area under the curve (AUC) of receiver operating curves with their 95% confidence intervals (CI). We calculated the sensitivity and specificity of the three approaches using a cut-off of 2.3 mm based on prior literature. RESULTS: We included 747 participants. The mean LUS thickness was greater with TA-US (3.8 ± 1.6 mm) compared with TV-US (3.5 ± 1.9 mm) or the combined measurement (3.2 ± 1.5 mm; P < .001). The AUC was 78% (95% CI: 69%-87%), 85% (95% CI: 79%-91%), and 88% (95% CI: 82%-93%), respectively (all with P < .001). The AUC difference between TA-US and the combined measurement was not significant (P = .057). A LUS below 2.3 mm would have predicted 9 (45%) of the 20 cases of uterine scar dehiscence using TA-US, 17 (85%) using TV-US, and 18 (90%) using the combined measurement (P < .01). CONCLUSION: The choice of ultrasound approach influences the measurement of the LUS thickness. The combination of the TA-US and TV-US seems to be superior for the detection of uterine dehiscence.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".