OP02.02: The 11–14 week anatomy scan: impact of sonographer training
Bibliographic record
Abstract
To assess the effect of training of sonographers in performing a fetal anatomic survey in conjunction with first-trimester nuchal translucency (NT) screening ultrasound (US). This is a prospective observational study of women presenting for NT screening. After informed written consent, six sonographers (all NT-qualified) measured fetal biometry, NT, and performed an anatomic survey following a standardized protocol. All studies were initially performed transabdominally. Transvaginal US was done when indicated. The maximum scan time was restricted to 30 minutes. After the initial phase (phase 1), and further sonographer education, additional women were studied (phase 2). The visualization of fetal anatomy was compared. Between July 2003 and February 2004, 227 singleton fetuses were examined (phase 1). Following further sonographer training, 216 singleton fetuses were examined between April and December, 2004 (phase 2). Mean gestational age was 12.5 weeks in both groups. Mean maternal body mass index (25.1 versus 24.9, p = 0.70) was comparable for both groups. Mean duration of scan (26.1 versus 24.5 minutes, p = 0.09) and % requiring transvaginal US (31.3% versus 23.1%, p = 0.07) were not significantly different between phase 1 and phase 2. NT was successfully measured in all fetuses. Visibility of fetal anatomic structures was compared in the following table: Visualization of fetal heart, spine and bladder significantly improved following further training. Sonographer education and experience improve the success rate of a complete fetal anatomic survey in a time-limited first-trimester US screening program.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".