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Record W2248953699 · doi:10.1159/000441844

Early-Onset Central Diabetes Insipidus due to Compound Heterozygosity for AVP Mutations

2015· article· en· W2248953699 on OpenAlexafffundabout
Karine Bourdet, Sophie Vallette, Johnny Deladoëy, Guy Van Vliet

Bibliographic record

VenueHormone Research in Paediatrics · 2015
Typearticle
Languageen
FieldMedicine
TopicElectrolyte and hormonal disorders
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
FundersCanadian Institutes of Health Research
KeywordsDiabetes insipidusLoss of heterozygosityMedicineEndocrinologyInternal medicineCompound heterozygosityVasopressinDiabetes mellitusPediatricsMutationGeneticsBiologyGeneAllele

Abstract

fetched live from OpenAlex

BACKGROUND: Genetic cases of isolated central diabetes insipidus are rare, are mostly due to dominant AVP mutations and have a delayed onset of symptoms. Only 3 consanguineous pedigrees with a recessive form have been published. CASE REPORT: A boy with a negative family history presented polyuria and failure to thrive in the first months of life and was diagnosed with central diabetes insipidus. Magnetic resonance imaging showed a normal posterior pituitary signal. A molecular genetic analysis of the AVP gene showed that he had inherited a previously reported mutation from his Lebanese father and a novel A>G transition in the splice acceptor site of intron 1 (IVS1-2A>G) from his French-Canadian mother. Replacement therapy resulted in the immediate disappearance of symptoms and in weight gain. CONCLUSIONS: The early polyuria in recessive central diabetes insipidus contrasts with the delayed presentation in patients with monoallelic AVP mutations. This diagnosis needs to be considered in infants with very early onset of polyuria-polydipsia and no brain malformation, even if there is no consanguinity and regardless of whether the posterior pituitary is visible or not on imaging. In addition to informing family counseling, making a molecular diagnosis eliminates the need for repeated imaging studies.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.410
Threshold uncertainty score0.721

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.097
GPT teacher head0.372
Teacher spread0.275 · 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

Citations17
Published2015
Admission routes3
Has abstractyes

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