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Neuropsychological Phenotype in Wolf-Hirschhorn Syndrome

2014· article· en· W2000199403 on OpenAlexvenueno aff
Maria Cristina Cossu, Annalisa Albergo, Claudia Galluzzi, Cristiana Stefani, Gabriella Antonucci

Bibliographic record

VenueJournal of Intellectual Disability - Diagnosis and Treatment · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomic variations and chromosomal abnormalities
Canadian institutionsnot available
Fundersnot available
KeywordsPhenotypeNeuropsychologyPsychologyNeuroscienceGeneticsCognitionBiologyGene

Abstract

fetched live from OpenAlex

The Wolf-Hirschhorn syndrome (WHS) is a rare genetic disorder that causes a range of intellectual disability from mild to severe. In this study, we used standard tools to psychometrically characterize the specific neuropsychological phenotype of WHS. We studied 57 individuals with WHS, ranging in age from 2.6 to 28.6 years representing 70% of the certified Italian WHS population. Results obtained by administering Griffiths’ Mental Developmental Scales and the Vineland Adaptive Behavior Scale revealed a typical WHS neuropsychological phenotype characterized by specific strengths and weaknesses. Despite their severe cognitive impairment, in both scales, patients showed better communication and social interaction skills compared to visuo-motor abilities. Results of our study could bring to the development of new and more effective treatments for individuals affected by WHS: based on neuropsychological phenotype description, it should be possible to design specific rehabilitation programs. These programs would then be aimed at improving rehabilitation protocols to optimize the developmental potential and personal independence of individuals with WHS and thus to improve their quality of life.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.018
GPT teacher head0.254
Teacher spread0.236 · 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 source (direct Gemma or distilled Codex), 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
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Intellectual Disability - Diagnosis and TreatmentSame topicGenomic variations and chromosomal abnormalitiesFrench-language works237,207