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Kanji-predominant alexia in advanced Alzheimer's disease

2009· article· en· W1985323359 on OpenAlexaff
Kei Nakamura, Kenichi Meguro, Hideki Yamazaki, J. Ishiaaki, Hiroshi Saito, Naohiro Saito, Masako Shimada, Satoshi Yamaguchi, Y. Shimada, Atsushi Yamadori

Bibliographic record

VenueActa Neurologica Scandinavica · 2009
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsHatch (Canada)
Fundersnot available
KeywordsKanaKanjiDyslexiaReading (process)PsychologyAudiologyComprehensionDementiaAlzheimer's diseaseReading comprehensionLinguisticsMedicineComputer scienceDiseaseArtificial intelligencePathologyChinese characters

Abstract

fetched live from OpenAlex

OBJECTIVES: Oral reading is preserved until the late stage of Alzheimer's disease (AD). However, it is unknown whether reading of kanji and kana is differentially impaired in Japanese AD patients. The purpose of this study was to examine alexic pattern in AD as related to two script systems. MATERIAL AND METHODS: In 18 severe AD patients, reading performance was compared among kana characters, monographic kanji words, and kana-transcribed words. Auditory comprehension was also examined. RESULTS: With increased severity of dementia, kanji reading was clearly more impaired than kana reading, which was relatively unaffected. Graphic complexity and frequency of the kanji influenced the performance variously among the patients. Dissociation between kanji reading and comprehension was also noted. CONCLUSION: As a result of multiple cognitive deficits, kanji reading is more impaired than kana reading in AD, but the difference is apparent only in the very late stage. Our findings suggest that kanji can be read correctly without meaning.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.029
GPT teacher head0.295
Teacher spread0.265 · 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

Citations10
Published2009
Admission routes1
Has abstractyes

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Same venueActa Neurologica ScandinavicaSame topicNeurobiology of Language and BilingualismFrench-language works237,207