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Record W4220903786 · doi:10.1177/10935266211045365

Impact of Congenital Heart Defects on the Developing Brain

2022· article· en· W4220903786 on OpenAlexaff
Xavier Navarri, Mubina Jovanovic, Delrue Marie-Ange, Van Doesburg Nicolas, Dal Soglio Dorothée, Catherine Fallet‐Bianco

Bibliographic record

VenuePediatric and Developmental Pathology · 2022
Typearticle
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsMedicineAutopsyIn uteroBrain damageFetusBirth weightPediatricsPregnancyLow birth weightCardiologyInternal medicine

Abstract

fetched live from OpenAlex

Objectives Congenital heart defects (CHD) are responsible for neurodevelopmental delays that were initially attributed to brain injury resulting from cardiac surgery. However, prenatal imaging have shown that brain anomalies are present at birth. The aim of this study was to assess in utero brain injuries before birth in fetuses/neonates with congenital cardiopathies. Methods A complete autopsy evaluation with detailed study of the cardiopathy and neuropathological study was performed in 40 fetuses/neonates. Syndromic congenital cardiopathies were excluded because of the potential other causes of brain injury. The patients were classified into two groups according to their term at death. Results Statistical analyses indicated the mean brain weight was not significantly different between subjects with different morphological types of congenital cardiopathies. However, the brain weight was at or below the fifth percentile in most third-trimester subjects compared to normal brain weight in second-trimester subjects. Low brain weight in third-trimester subjects was also associated with frequent lesions similar to those described in preterm infants, with a particular involvement of white matter and its components. Conclusions These observations allowed us to establish the timing and impact of prenatal neuropathological lesions on brain development, and to correlate them with imaging data reported in the literature.

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 score0.007
Threshold uncertainty score0.399

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 head0.021
GPT teacher head0.286
Teacher spread0.265 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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