Two-dimensional sonographic assessment of maximum placental length and thickness in the second trimester: a reproducibility study
Bibliographic record
Abstract
OBJECTIVES: To determine the most reproducible method for the sonographic measurement of placental length. METHODS: A prospective study of women with singleton pregnancies who underwent sonographic measurement of placental dimensions during mid-gestation. Two sonographers independently determined placental length using three different approaches (linear, curve-linear and panoramic) and placental thickness. Reproducibility was assessed by the Bland-Altman method and Interclass Correlation Coefficient (ICC). RESULTS: Overall 34 women were included in the study. The curve-linear approach for the measurement of placental length was associated with the highest reproducibility (mean inter-observer difference of -0.10 cm) compared to the linear and panoramic approaches (mean difference -0.15 cm and -0.29 cm, respectively). Similarly, the ICC was highest for the curve-linear length approach (0.974) compared with the linear length and panoramic length approaches (0.956 and 0.926, respectively). Measurements of maximum placental thickness was also associated with a very good ICC (0.954). CONCLUSIONS: The curve-linear method for the measurement of placental length in the 2nd trimester appears to be the most reproducible approach. This technique may prove useful as an adjunct screening method, along with uterine artery Doppler and maximum placental thickness, to screen for major placental complications of pregnancy in the second trimester.
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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 | 0.026 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".