VP04.03: Assessment of maternal anxiety among women undergoing an early comprehensive fetal anatomy scan
Bibliographic record
Abstract
Carrying a fetus at increased risk of anomalies is a risk factor for maternal anxiety and delaying the routine anatomic ultrasound to 18–22 weeks may contribute to prolonged elevations in pregnancy-related anxiety. Advances in ultrasound technology now permit the detection of major fetal anomalies earlier in pregnancy. The purpose of this study was to evaluate the impact of an early comprehensive anatomy scan (ECAS) on the level of anxiety experienced by pregnant women at higher risk of having a fetus with structural anomalies. Prospective observational study of pregnant women with singleton pregnancies referred to the ECAS clinic (13–16 weeks of gestation) for standard indications (SOGC, 2018). Pregnancy-related anxiety levels were evaluated using the Spielberger State-Trait Anxiety Inventory (STAI), administered before and after the ECAS assessment. 21 singleton pregnant women were recruited. High levels of pregnancy-related anxiety were found in most of the participants before the ECAS assessment which markedly decreased post-scan, after disclosure of the results. The mean STAI score pre-ECAS was 48 (33–77). The mean score after disclosure of the ultrasound results was 32 (20–60). A decrease in the STAI score was observed in 86% (18/21) of the participants with a mean change in score of -16 (-37 – +3). Pregnancy-related anxiety among women at increased risk of fetal anomalies was very high before ECAS using the standardised STAI score and significantly decreased immediately following disclosure of the scan results. This confirms that in addition to early detection of fetal abnormalities, pregnant women at increased risk of fetal anomalies benefit psychologically from the option of an early complete anatomy scan.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".