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Dysgraphia in Relation to Cognitive Performance in Patients with Alzheimer’s Disease

2013· article· en· W2008870810 on OpenAlexvenueno aff
Emanuela Onofri, Marco Mercuri, MariaLucia Salesi, Salvatore Ferrara, Giulia Maria Troili, C. Simeone, Max Rapp Ricciardi, Serafino Ricci, Trevor Archer

Bibliographic record

VenueJournal of Intellectual Disability - Diagnosis and Treatment · 2013
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsnot available
Fundersnot available
KeywordsDysgraphiaAlzheimer's diseaseCognitionPsychologyRelation (database)DiseaseCognitive psychologyNeuroscienceMedicineInternal medicineComputer scienceDyslexiaLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

Dysgraphia has been observed in patients presenting mild to moderate levels of Alzheimer’s disease (AD) in several studies. In the present study, 30 AD patients and 30 matched healthy controls, originating from the Lazio region, Rome, Italy, were examined on tests of letter-writing ability and cognitive performance over a series of 10 test days that extended over 19 days (Test days: 1, 3, 5, 7, 9 11, 13, 15, 17, and 19). Consistent deficits by the AD patients over the initial cognition test (PQ1), 2nd cognition test (PQ2) and the difference between them (D∆), expressing deterioration, and writing-time compared the group of healthy control subjects were obtained. Furthermore, the performances of the AD patients on the PQ1, D∆ and writing-time, but not the PQ2, tests deteriorated from the 1st five days of testing (Days 1-9) to the 2nd five days (11-19). Both AD patients’ and healthy controls’ MMSE scores were markedly and significantly correlated with performance of PQ1, writing-time and PQ2. The extent of dysgraphia and progressive deficits in the AD patients implicate multiple brain regions in the loss of functional integrity.

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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.033
GPT teacher head0.271
Teacher spread0.239 · 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

Citations23
Published2013
Admission routes1
Has abstractyes

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Same venueJournal of Intellectual Disability - Diagnosis and TreatmentSame topicNeurobiology of Language and BilingualismFrench-language works237,207