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Record W3200239379 · doi:10.1002/acn3.51448

L1CAM variants cause two distinct imaging phenotypes on fetal MRI

2021· article· en· W3200239379 on OpenAlexaff
Andrea Accogli, Stacy Goergen, Giana Izzo, Kshitij Mankad, Karina Krajden Haratz, Cecilia Parazzini, Michael Fahey, Lara Menzies, Júlia Baptista, Lucia Carpineta, Domenico Tortora, Ezio Fulcheri, Valerio Gaetano Vellone, D. Paladini, Luigina Spaccini, Valentina Toto, Claire Trayers, Liat Ben‐Sira, Adi Reches, G. Malinger, Vincenzo Salpietro, Patrizia De Marco, Myriam Srour, Federico Zara, Valeria Capra, Andrea Rossi, Mariasavina Severino

Bibliographic record

VenueAnnals of Clinical and Translational Neurology · 2021
Typearticle
Languageen
FieldMedicine
TopicFetal and Pediatric Neurological Disorders
Canadian institutionsMcGill UniversityMontreal Children's Hospital
FundersMinistero della Salute
KeywordsMedicineBrainstemDysgenesisHypoplasiaAqueductal stenosisFetusAnatomyHydrocephalusCerebellar hypoplasia (non-human)DysplasiaPathologyWhite matterPhenotypeMagnetic resonance imagingCerebellumRadiologyInternal medicineBiologyPregnancy

Abstract

fetched live from OpenAlex

Data on fetal MRI in L1 syndrome are scarce with relevant implications for parental counseling and surgical planning. We identified two fetal MR imaging patterns in 10 fetuses harboring L1CAM mutations: the first, observed in 9 fetuses was characterized by callosal anomalies, diencephalosynapsis, and a distinct brainstem malformation with diencephalic-mesencephalic junction dysplasia and brainstem kinking. Cerebellar vermis hypoplasia, aqueductal stenosis, obstructive hydrocephalus, and pontine hypoplasia were variably associated. The second pattern observed in one fetus was characterized by callosal dysgenesis, reduced white matter, and pontine hypoplasia. The identification of these features should alert clinicians to offer a prenatal L1CAM testing.

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 score0.067
Threshold uncertainty score0.407

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 head0.112
GPT teacher head0.407
Teacher spread0.295 · 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

Citations22
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueAnnals of Clinical and Translational NeurologySame topicFetal and Pediatric Neurological DisordersFrench-language works237,207