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Record W2862212167 · doi:10.5770/cgj.21.291

Effect of Methylphenidate for Apathy on Visual Attention Scanning Behavior: a Pilot Study

2018· article· en· W2862212167 on OpenAlexafffundvenue
Sarah Chau, Nathan Herrmann, Moshe Eizenman, Krista L. Lanctôt

Bibliographic record

VenueCanadian Geriatrics Journal · 2018
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsSunnybrook HospitalUniversity of TorontoSunnybrook Health Science Centre
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsApathyInterquartile rangeMedicineNeuropsychologyMethylphenidatePsychiatryCognitionAudiologyInternal medicineAttention deficit hyperactivity disorder

Abstract

fetched live from OpenAlex

BackgroundThe purpose of this pilot study was to explore the potential of eye-tracking technology in monitoring symptoms and predicting outcomes in apathetic Alzheimer’s disease (AD) patients treated with methylphenidate (MTP).MethodsNeuropsychological tests and eye-tracking measurements were completed at baseline and following at least four weeks of treatment with MTP (5–10 mg BID). Eye-movements were measured while patients viewed novel and social stimuli. Cognition, behavior, and apathy were assessed using the Standardized Mini-Mental State Exam (sMMSE), Neuropsychiatric Inventory, and Apathy Evaluation Scale (AES), respectively.ResultsNine patients were included in the analysis (age: median=75, interquartile range=8; sMMSE: median=22, interquartile range=14). Spearman correlations showed that improvement on the AES was associated with increased visual attention towards novel stimuli (ρ7=-0.809, p=.008). Additionally, lower baseline attention towards social images was associated with improvement on the AES (ρ7=0.905, p=.001).ConclusionsEye-tracking techniques can be developed as an objective and nonverbal method of monitoring symptoms and treatment outcomes in AD patients.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.213
Threshold uncertainty score0.504

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.368
Teacher spread0.340 · 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 teacher head, 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

Citations2
Published2018
Admission routes3
Has abstractyes

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