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Record W2511600371 · doi:10.1159/000448994

Association between Fetal Cerebral Ventriculomegaly and Platelet Alloimmunisation

2016· article· en· W2511600371 on OpenAlexaff
Gabriella Martillotti, Françoise Rypens, Michéle David, Nancy Catalfamo, Johanne Dubé, Catherine Taillefer, Christian Lachance, François Audibert

Bibliographic record

VenueFetal Diagnosis and Therapy · 2016
Typearticle
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsMedicineFetusNeonatal alloimmune thrombocytopeniaVentriculomegalySubclinical infectionPregnancyPlateletProspective cohort studyAntibodyIsoantibodiesInternal medicineObstetricsImmunology

Abstract

fetched live from OpenAlex

INTRODUCTION: Fetal and neonatal alloimmune thrombocytopenia (FNAIT) is a rare condition that may lead to intracerebral haemorrhage (ICH) in the fetus or neonate. Platelet alloimmunisation causing FNAIT has been described in association with fetal cerebral ventriculomegaly (VM), presumably due to subclinical ICH. The objective of this study was to assess the association between fetal VM and platelet alloimmunisation. METHODS: This is a case series of pregnancies with fetal VM screened for platelet alloantibodies from 2003 to 2012. Cases of multiple pregnancies, structural anomalies, aneuploidies, or congenital infection were excluded. RESULTS: Of 45 pregnancies with fetal VM that were screened for platelet alloantibodies, 5 (11%) were positive. There was only one antenatal ICH, with confirmed fetal severe thrombocytopenia before termination of pregnancy. The other cases were treated with intravenous immunoglobulins without prior fetal blood sampling. No other case of neonatal thrombocytopenia was confirmed. CONCLUSIONS: The prevalence of platelet alloimmunisation was high in this series of fetal VM. Prospective large studies are needed to confirm the role of platelet alloimmunisation in fetal VM.

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 score0.319
Threshold uncertainty score0.326

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 head0.018
GPT teacher head0.256
Teacher spread0.238 · 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

Citations5
Published2016
Admission routes1
Has abstractyes

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