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Record W2031153607 · doi:10.1159/000380821

The Contribution of MRI after Fetal Anomalies Have Been Diagnosed by Ultrasound: Correlation with Postnatal Outcomes

2015· article· en· W2031153607 on OpenAlexaff
Bero O. Verburg, A. Michelle Fink, Karen Reidy, Ricardo Palma‐Dias

Bibliographic record

VenueFetal Diagnosis and Therapy · 2015
Typearticle
Languageen
FieldMedicine
TopicFetal and Pediatric Neurological Disorders
Canadian institutionsRoyal Ottawa Mental Health Centre
Fundersnot available
KeywordsMedicineFetusUltrasoundMagnetic resonance imagingPregnancyRadiologyObstetricsPrenatal diagnosisReferral

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim of this study was to investigate the additional value of fetal magnetic resonance imaging (MRI) in the assessment and management of fetuses with abnormal findings on ultrasound. METHODS: A total of 257 patients who had fetal MRI following the ultrasound diagnosis of a fetal anomaly, or were at high risk, were included. The patients were grouped by referral category for fetal MRI. Fetal MRI was compared to ultrasound in the detection of anomalies, i.e. whether additional findings were identified and if this changed diagnosis, prognosis and management during pregnancy. RESULTS: Ultrasound findings were confirmed on fetal MRI in 89% of the cases. Additional findings were seen with MRI in 28% of all patients. The diagnosis changed in 21% and the prognosis in 19% of the cases. Perinatal management changed in 8%. The antenatal findings were confirmed in all cases that had a postmortem examination following termination of pregnancy. In all the pregnancies that continued to delivery and for which the postnatal outcome is known, the findings correlated in 97% of the cases. CONCLUSION: Fetal MRI provided additional detection of fetal anomalies, leading to a change in diagnosis and prognosis in 19% of the cases. Neonatal and postmortem findings mostly confirmed the fetal MRI diagnosis, suggesting it to be a useful tool for clinical decision making in perinatal management.

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 score0.009
Threshold uncertainty score0.397

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 head0.012
GPT teacher head0.241
Teacher spread0.230 · 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

Citations29
Published2015
Admission routes1
Has abstractyes

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