Normal pericardial fluid in the fetus: color and spectral Doppler analysis
Bibliographic record
Abstract
OBJECTIVES: To estimate the incidence of sonographic identification of pericardial fluid in normal fetuses and to evaluate the flow pattern of pericardial fluid by using color and spectral Doppler techniques. METHODS: We evaluated 27 normal fetuses for the presence of pericardial fluid by using gray-scale two-dimensional and M-mode ultrasound, and color and spectral Doppler techniques. RESULTS: Pericardial fluid was detected in 52% of cases by two-dimensional and M-mode ultrasound and in 81% of cases by color Doppler. The pericardial fluid moved towards the ventricles during systole and towards the atria during diastole. In 9 of 22 fetuses with pericardial fluid identified by color Doppler, spectral waveforms were obtained. The waveforms confirmed the bidirectional flow pattern identified at color Doppler. In six cases there was monophasic systolic and biphasic diastolic flow. In the remaining three cases, the flow was monophasic during both systole and diastole. CONCLUSIONS: Pericardial fluid can be identified with color Doppler in the majority of normal fetuses. It characteristically shows bidirectional flow as it moves with ventricular systole and diastole. Spectral waveforms can be obtained from the pericardial fluid. The presence of pericardial fluid per se should not be considered as abnormal. Color-coded pericardial fluid should not be mistaken for coronary artery blood flow.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".