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Record W2002634186 · doi:10.1358/mf.2000.22.2.796074

Pharmaco-EEG test dose response predicts cholinesterase inhibitortreatment outcome in Alzheimer's disease

2000· article· en· W2002634186 on OpenAlexaff
V Knott, Erich Mohr, Colleen Mahoney, Vadim Ilivitsky

Bibliographic record

VenueMethods and Findings in Experimental and Clinical Pharmacology · 2000
Typearticle
Languageen
FieldNeuroscience
TopicEEG and Brain-Computer Interfaces
Canadian institutionsRoyal Ottawa Mental Health CentreUniversity of Ottawa
Fundersnot available
KeywordsElectroencephalographyCholinesteraseDementiaAlpha (finance)Alzheimer's diseaseCentral nervous system diseaseDegenerative diseaseMedicinePsychologyInternal medicineAnesthesiaDiseasePsychometricsDevelopmental psychologyPsychiatry

Abstract

fetched live from OpenAlex

Previous investigations have indicated that a single dose pharmaco-EEG may predict the outcome of 4-7 weeks of tetrahydroaminoacridine (THA) treatment in dementia of the Alzheimer type (DAT). This open trial study further examined the relationship of quantitative EEG in relation to treatment response by assessing 24 probable DAT patients at baseline, 2 h after their first oral dose (30 mg), and after 12 weeks of THA treatment. Compared to EEG norms, patients, in general, evidenced EEG slowing, as shown by excessive slow (theta) and diminished fast (alpha and beta) wave power as well as reduced mean frequencies which were present prior to treatment as well as at the end of treatment. The EEG of patients exhibiting stable or improved scores on the Mini-Mental State examination (MMSE) at 12 weeks showed a significantly faster baseline mean alpha frequency as well as a significant reduction in relative theta power following the single THA test dose compared to deteriorated patients. A discriminant analysis using test dose response EEG variables correctly classified 75-79% of these two patient groups, suggesting that this procedure may be a useful approach for optimizing patient selection for antidementia treatments.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.100
GPT teacher head0.494
Teacher spread0.393 · 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.

Study designBench or experimental
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

Citations25
Published2000
Admission routes1
Has abstractyes

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