MétaCan
Menu
Back to cohort
Record W2001175914 · doi:10.1159/000350269

Reassessment of the Normal Fetal Cisterna Magna during Gestation and an Alternative Approach to the Definition of Cisterna Magna Dilatation

2013· article· en· W2001175914 on OpenAlexaff
Richard Brown

Bibliographic record

VenueFetal Diagnosis and Therapy · 2013
Typearticle
Languageen
FieldMedicine
TopicFetal and Pediatric Neurological Disorders
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsCisterna magnaGestationGestational ageMedicineFetusReference rangePopulationPregnancyObstetricsInternal medicineBiologyCerebrospinal fluid

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.370
Threshold uncertainty score0.285

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.036
GPT teacher head0.269
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueFetal Diagnosis and TherapySame topicFetal and Pediatric Neurological DisordersFrench-language works237,207