Screening for small‐for‐gestational‐age fetuses
Bibliographic record
Abstract
Abstract Introduction It is well established that correct antenatal identification of small‐for‐gestational‐age (SGA) fetuses reduces their risk of adverse perinatal outcome with long‐term consequences. Ultrasound estimates of fetal weight (EFWus) are the ultimate tool for this identification. It can be conducted as a “universal screening”, that is, all pregnant women at a specific gestational age. However, in Denmark it is conducted as “selective screening”, that is, only on clinical indication. The aim of this study was to assess the performance of the Danish national SGA screening program and the consequences of false‐positive and false‐negative SGA cases. Material and methods In this retrospective cohort study, we included 2928 women with singleton pregnancies with due dates in 2015. We defined “risk of SGA” by an EFWus ≤ −15% of expected for the gestational age and “SGA” as birthweight ≤−22% of expected for gestational age. Results At birth, the prevalence of SGA was 3.3%. The overall sensitivity of the Danish screening program was 62% at a false‐positive rate of 5.6%. Within the entire cohort, 63% had an EFWus compared with 79% of the SGA cases. The sensitivity was 79% for those born before 37 weeks of gestation but only 40% for those born after 40 weeks of gestation. The sensitivity was also associated with birthweight deviation; 73% among extreme SGA cases (birthweight deviation ≤−33%) and 55% among mild SGA (birthweight deviation between −22% and −27%). False diagnosis of SGA was associated with an increased rate of induction of labor (ORadj = 2.51, 95% CI 1.70‐3.71) and cesarean section (ORadj = 1.44, 95% CI 0.96‐2.18). Conclusions The performance of the Danish national screening program for SGA based on selective EFWus on clinical indication has improved considerably over the last 20 years. Limitations of the program are the large proportion of women referred to ultrasound scan and the low performance post‐term.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".