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Record W1966872042 · doi:10.1055/s-2000-9418

RECURRENT FAMILIAL NEONATAL DEATHS: HEREDITARY SURFACTANT PROTEIN B DEFICIENCY

2000· article· en· W1966872042 on OpenAlexaff
Chad Andersen, Jennifer Ramsay, Lawrence M. Nogee, Jay Shah, Susan E. Wert, Bosco Paes, Małgorzata J.M. Nowaczyk

Bibliographic record

VenueAmerican Journal of Perinatology · 2000
Typearticle
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsMcMaster UniversityHamilton Health Sciences
FundersNational Heart, Lung, and Blood Institute
KeywordsMedicinePediatricsAutopsyHyalineDiseasePrenatal diagnosisNeonatal deathGenetic counselingConsanguinityPathologyPregnancyFetusGenetics

Abstract

fetched live from OpenAlex

Hereditary surfactant protein B (SP-B) deficiency is an uncommon autosomal recessive lung disorder that causes hypoxemic respiratory failure in mature, morphologically normal infants. Recognition and diagnosis of this condition is of paramount importance, as it has significant implications for future pregnancies with a recurrence risk of 25%. In a family with three neonatal deaths over 20 years, SP-B deficiency was diagnosed following the death of the fourth affected infant. Previous deaths were mistakenly attributed to hyaline membrane disease (HMD), congenital Mycoplasma hominis infection, and pulmonary hypertension, however, following the diagnosis in the proposita, SP-B deficiency was also confirmed in her deceased siblings by immunohistochemical staining of autopsy specimens. This case highlights the presentation, postnatal course, diagnosis, and therapeutic options of SP-B deficiency in addition to the mode of inheritance and the possibility of antenatal diagnosis. Genetic consultation is imperative in the investigations of recurrent neonatal deaths, especially in cases of remote events. The recent enormous advances in human genetics have shown that many conditions previously ascribed to environmental agents have a genetic basis.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.989
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.337
Teacher spread0.316 · 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.

Study designOther design
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

Citations20
Published2000
Admission routes1
Has abstractyes

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