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Record W4396808725 · doi:10.1002/mdc3.14052

Atypical Presentations of Huntington Disease‐like 2 in South African Individuals

2024· article· en· W4396808725 on OpenAlexaff
Heena Narotam, Mark Guttman, Ludo van Hillegondsberg, Riaan van Coller, Amanda Krause, Jonathan Carr

Bibliographic record

VenueMovement Disorders Clinical Practice · 2024
Typearticle
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsCentre for Movement DisordersUniversity of Toronto
Fundersnot available
KeywordsChoreaHuntington's diseasePenetranceDystoniaRating scaleDiseaseMedicinePsychologyAudiologyPhysical medicine and rehabilitationPsychiatryInternal medicineDevelopmental psychologyPhenotypeGeneticsBiology

Abstract

fetched live from OpenAlex

BACKGROUND: Huntington disease-like 2 (HDL2) is a neurodegenerative disorder, affecting only individuals of African ancestry. Full penetrance occurs in individuals with 40 repeats or more. OBJECTIVE: To describe the phenotypic variability of HDL2 in a group of mixed ancestry individuals from South Africa. METHODS: Eight patients were assessed with analysis of repeat size and magnetic resonance brain imaging. We applied the Unified Huntington's Disease Rating Scale (UHDRS), but in deceased patients (4), this was estimated from video material. RESULTS: Cognitive domains were more severely affected than motor; UHDRS motor scores were notable for bradykinesia, and to a slightly lesser extent, for rigidity and dystonia; a single patient had marked chorea. Repeat lengths ranged from 45 to 63 (median, 52). CONCLUSION: This South African group of mixed ancestry HDL2 individuals presented with severe cognitive and behavioral impairments, with lesser degrees or absence of chorea. This presentation is possibly related to large repeat sizes.

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: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.064
GPT teacher head0.393
Teacher spread0.329 · 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 designCase report
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

Citations2
Published2024
Admission routes1
Has abstractyes

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