Reassessment of the Normal Fetal Cisterna Magna during Gestation and an Alternative Approach to the Definition of Cisterna Magna Dilatation
Bibliographic record
Abstract
INTRODUCTION: Cisterna magna (CM) measurement constitutes part of the sonographic assessment of the posterior fossa. CM enlargement (ECM) is defined as a measurement exceeding 10 mm, although it has previously been noted that the CM varies in size with gestation. Existing data do not appear to reflect observations regarding CM biometry within our population and this study was therefore undertaken in order to re-evaluate CM biometry. MATERIALS AND METHODS: Data from 4,750 normal pregnancies between 15 and 32 weeks of gestation were collected and used to construct a reference range for the CM. RESULTS: Regression analysis was used to model CM across gestational age and thereby define the upper limits for normal CM measurements across gestation. The CM increases with gestation. These data suggest that a 10-mm cut-off underestimates ECM, notably in the gestational age period below 24 weeks, whilst overestimating isolated ECM beyond this. Differences in CM measurements between genders were confirmed (p < 0.0001). CONCLUSIONS: Defining ECM based upon a cut-off of 10 mm across all gestations may be inappropriate given the variation observed with gestational age. More accurate identification of fetuses with, in particular, isolated ECM may facilitate more precise evaluation of the clinical significance of this finding.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".