Feasibility and limitations of the prenatal sonographic assessment of choanal flow and neonatal implications
Bibliographic record
Abstract
Objective To evaluate the factors associated with the prenatal detection of choanal flow (CF) in a normal population. Methods Fifty pregnant women underwent CF evaluation using B-Mode and color Doppler. Screening for CF began at 22 weeks, standardized according to two section planes: sagittal and transverse. CF was considered positive when flow was seen, and negative if no flow was detected after 1 minute. The screening was repeated monthly until flow was observed. We assessed maternal BMI, fetal gender, gestational age at the first detection of flow, placental site, visibility noted by the sonographer, nasal asymmetry, and possible nostril dilatation. Results Choanal flow was established in all patients except two cases where the fetal face was consistently downwards (48/50). The gestational age at first detection of choanal flow was 28 weeks ± 3.5 weeks. Flow was unilateral in 56.3% of cases and bilateral in 43.8% of cases. Visibility assessed by the operator was rated as good in 72% of cases, average in 20%, and poor in 8%. The only factor significantly associated with the gestational age at first detection of choanal flow was visibility ( p = 0.006). Conclusion The average gestational age for the detection of choanal flow is 28 weeks. Relying solely on second-trimester morphological ultrasound may result in missed detections.
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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 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".