OP06.04: Comprehensive audit of estimation of fetal weight in preterm and term population in a tertiary centre
Bibliographic record
Abstract
To identify 1) main driver of error of estimated fetal weight (EFW) by ultrasound (US) in preterm and term population, and 2) discrepancies in biometric parameters between populations. A retrospective audit was conducted in a tertiary centre in Ottawa, Canada in 2022. We included all US performed from 24 to 41 weeks of gestation from singleton pregnancies. Images performed within 14 days of delivery were reviewed. Data was analysed by 2 independent examiners. The ISUOG guideline for fetal biometry scoring system was used. Each sonographic parameter scored 1(presence) or 0(absence) point for anatomical landmarks when measuring biparietal diameter (BPD), head circumference (HC), abdominal circumference (AC), and femur length (FL), totalising a score of 24. Potential confounders, fetal presentation, amniotic fluid (AF), and machine model, were analysed. Fisher's exact or chi-square tests were used for comparisons. Statistical significance was set at P < .05. A total of 112 preterm and 104 term scans were reviewed. The mean total score was 17.4 ± 2.9 and 16.9 ± 2.9 for preterm and term. For the preterm scans, BPD was main parameter driving error (26%), and CSP was missing in 69.6% of images. HC, AC, and FL's main drivers of error were absence of CSP (70%), incorrectly placed calipers (35%) and femur occupying less than half the image (47%). For the term scans, AC was the main parameter driving error (31%), and symmetrical plane was not captured in 54% of images. BPD/HC and FL's main drivers of error were absence of CSP (29%) and incorrectly placed calipers (47%). Overall, there were statistically significant differences when comparing quality of AC and FL images between populations, with lower error in the preterm group. No differences were found when adjusted for presentation, AF or machine model. EFW directly impacts clinical decision-making and is a quality measure of antenatal care. We identified main sonographic drivers of error in preterm and term group. Education and training to increase accuracy of EFW are in progress.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".