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Record W2085770920 · doi:10.1002/ana.77777

Hereditary motor and sensory neuropathy with agenesis of the corpus callosum

2003· review· en· W2085770920 on OpenAlexafffundabout
Nicolas Dupré, Heidi Howard, Jean Mathieu, George Karpati, Michel Vanasse, Jean‐Pierre Bouchard, Stirling Carpenter, Guy A. Rouleau

Bibliographic record

VenueAnnals of Neurology · 2003
Typereview
Languageen
FieldMedicine
TopicFetal and Pediatric Neurological Disorders
Canadian institutionsHôpital de l'Enfant-JésusUniversité LavalUniversité de MontréalCentre Hospitalier Universitaire Sainte-JustineMontreal Neurological Institute and HospitalMcGill University
FundersCanadian Institutes of Health Research
KeywordsAgenesis of the corpus callosumHereditary motor and sensory neuropathyCorpus callosumHypotoniaAmyotrophyPolyneuropathyAgenesisMedicineFounder effectPediatricsNeuroscienceDiseasePsychologyPathologyGeneticsAnatomyBiologyAtrophyGeneAlleleHaplotype

Abstract

fetched live from OpenAlex

Hereditary motor and sensory neuropathy associated with agenesis of the corpus callosum (OMIM 218000) is an autosomal recessive disease of early onset characterized by a delay in developmental milestones, a severe sensory-motor polyneuropathy with areflexia, a variable degree of agenesis of the corpus callosum, amyotrophy, hypotonia, and cognitive impairment. Although this disorder has rarely been reported worldwide, it has a high prevalence in the Saguenay-Lac-St-Jean region of the province of Quebec (Canada) predominantly because of a founder effect. The gene defect responsible for this disorder recently has been identified, and it is a protein-truncating mutation in the SLC12A6 gene, which codes for a cotransporter protein known as KCC3. Herein, we provide the first extensive review of this disorder, covering epidemiological, clinical, and molecular genetic 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 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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.003

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 head0.104
GPT teacher head0.321
Teacher spread0.218 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations95
Published2003
Admission routes3
Has abstractyes

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