Evaluating the correlation between amniotic fluid volume and estimated fetal weight in healthy pregnant women
Bibliographic record
Abstract
OBJECTIVES: The establishment of cut-offs for normal amniotic fluid volume (AFV) is valuable to predict perinatal outcomes. However, the most common methods to measure AFV are not accurate enough. It is important to understand factors that may be able to increase the accuracy of the calculation of AFV cut-off values. The objective of this study was to verify the correlation between AFV and estimated fetal weight (EFW). METHODS: Records from almost 7,000 patients between 2012 and 2017 were accessed through hospital databases. The AFV measurements included in our analysis were obtained using the maximum vertical pocket technique. RESULTS: AFV was positively correlated with EFW in the overall, male and female samples; however, the magnitude of the association was small (0.1<r<0.3). A moderate (0.3≤r<0.5) association between AFV and EFW was found specifically during mid- to late-gestation. CONCLUSIONS: The incorporation of EFW together with other factors (e.g., gestational age, fetus sex) may increase the accuracy of the AFV cut-offs calculation and, ultimately, reduce morbidity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".