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Specific language impairment as the prominent feature in a patient with a low‐level trisomy 21 mosaicism

2007· article· en· W1966091424 on OpenAlexaff
Ariane Paoloni‐Giacobino, Nicole Lemieux, Emmanuelle Lemyre, J. Lespinasse

Bibliographic record

VenueJournal of Intellectual Disability Research · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomic variations and chromosomal abnormalities
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsIntellectual disabilityTrisomySupernumeraryDown syndromeFluorescence in situ hybridizationKaryotypeSpecific language impairmentBiologyGeneticsAneuploidyPhenotypePsychologyPathologyChromosomeMedicineDevelopmental psychologyGeneAnatomy

Abstract

fetched live from OpenAlex

BACKGROUND: The extent and severity of the disabilities is variable among individuals with Down syndrome, although generally characterized by a range of physical and intellectual conditions, including language impairment. Whether the language deficit is due to the intellectual disability (ID) or associated to the supernumerary or portion of chromosome 21 is still debated. METHODS: Karyotyping was performed on blood lymphocyte and skin fibroblasts. Fluorescence in situ hybridization analysis was performed on cultured lymphocytes and buccal smear cells. RESULTS: The trisomy 21 (T21) mosaicism was characterized by 0.7-10% of mosaic cells in the different tissues, in a 14-year-old girl presenting an intellectual development within the normal range and specific language impairment (SLI) as the only prominent feature. CONCLUSION: This case illustrates the wide range of phenotypical abnormalities possibly associated with T21 mosaicism. We propose that SLI is indeed a phenotypic trait specific to Down syndrome rather than subsequent to the ID most often associated to the syndrome.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.135
Threshold uncertainty score0.306

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 integrity0.0000.001
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 head0.025
GPT teacher head0.312
Teacher spread0.288 · 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 designBench or experimental
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
Published2007
Admission routes1
Has abstractyes

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