VP17.06: Estimation of fetal weight in patients with high BMI: challenging but accurate?
Bibliographic record
Abstract
To verify the accuracy of sonographic estimation of fetal weight in obese patients compared to normal body mass index (BMI) patients. We conducted a retrospective cohort study from January to December 2020 at both sites of a tertiary hospital. Estimated fetal weight (EFW) was compared to the actual birth weight (ABW) in grams obtained by chart review. We used Hadlock 3 to calculate EFW. Absolute error was calculated as (ABW-EFW) and percentage error (%err) as ((ABW-EFW)/ABW)x100. All patients above 36 weeks who had an ultrasound within 14 days of delivery were included; reported congenital anomalies and charts with undocumented BMI were excluded. We stratified results by BMI class and hospital campus. Based on the literature, a percentage error less or equal 10% was considered acceptable. Descriptive statistics and one-way ANOVA were employed to describe and compare results. Statistical significance was set at p < .05. From a total of 6019 patients, a sample size of 493 was obtained after exclusion criteria. Patients were distributed by BMI as per the WHO classification: normal (11.6%), overweight (28.6%), obesity classes I (28.4%), II (15.6%), III (12%), and IV (4%). Overall, mean absolute error was 61±268g and mean %err 1.77±7.79% for all patients. When comparing normal BMI vs obesity, mean absolute and %err were not statistically different for neither BMI class (P = .56 and p = .48, respectively), nor for different campus (P = .86 and p = .96, respectively). The mean %err for normal BMI was 0.5 vs 1.93 for BMI > 25 (P = .19). Almost 21% (n = 103) of cases presented %err above the acceptable 10% (%err varied from -21.5% to 23.2%). Our results showed that the %err was comparable among all BMI categories and no statistically significant difference was found when comparing obese with normal BMI patients. This is aligned with previously reported studies. When comparing hospital sites, no inter-observer differences were found. As for further directions, a quality improvement audit will be conducted to individually assess images in which %err was above 10%.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 | 0.001 |
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".