Cervical assessment certification and its impact on performance quality in the context of universal cervical screening
Bibliographic record
Abstract
OBJECTIVE: To assess the impact of the introduction of universal transvaginal cervical screening and certification on the quality of cervical length ultrasound images. METHODS: weeks) before (period A, 2015-2017) and after (period B, 2017-2019) the introduction of universal transvaginal cervical length screening. Independent observers blindly evaluated the images obtained for cervical length using a qualitative scoring method based on five criteria, according to the Fetal Medicine Foundation. RESULTS: In all, 6013 patients met the inclusion criteria, 3333 in period A and 2680 in period B. Maternal characteristics and risk factors for preterm birth were similar between the two periods. The acceptance of transvaginal cervical length measurement in period B was 95.5% in the overall cohort and 100% in the subgroup of high-risk patients. The quality score was significantly higher in period B than in period A. Among the image quality criteria, the anterior/posterior ratio, the correct magnification of the images, and the calipers' placement contributed significantly to the improved quality score in period B. Most of the sonographers performed better in period B, irrespective of the years of experience, but certificate holders obtained higher scores than non-certified sonographers, particularly those in mid-career. The identification of short cervix was significantly higher in period B than in period A. CONCLUSION: The implementation of universal transvaginal cervical length screening and the certification process are associated with improved quality of cervical length images, even among expert sonographers and in the presence of anatomical pitfalls.
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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 | 0.007 | 0.044 |
| 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 | 0.002 |
| Research integrity | 0.000 | 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".