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Record W2018990521 · doi:10.1002/ajmg.b.30409

Analysis of the 5HT‐2A T102C receptor polymorphism and psychotic symptoms in Alzheimer's disease

2006· article· en· W2018990521 on OpenAlexfundno aff
David Craig, Caroline Donnelly, Dominic Hart, Robyn Carson, Peter Passmore

Bibliographic record

VenueAmerican Journal of Medical Genetics Part B Neuropsychiatric Genetics · 2006
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsnot available
FundersAlzheimer Society
KeywordsSerotonergicGenotypeDementiaPolymorphism (computer science)EtiologyAlzheimer's diseasePsychology5-HT receptorDiseaseAllelePsychosisPsychiatryInternal medicineMedicineReceptorSerotoninGeneticsBiology

Abstract

fetched live from OpenAlex

Although the aetiology of psychotic symptoms in Alzheimer's disease (AD) is multi-factorial, alterations in serotonergic neurotransmission are often implicated. Polymorphisms of the serotonin receptor 5HT-2A are associated with hallucinatory symptoms and delusions in demented and non-demented cohorts. This study examined the role of the 5HT-2A T102C polymorphism in influencing psychotic symptoms in a large Northern Ireland AD population (n = 406, mean MMSE 13/30). Forty-eight percent of patients experienced delusional symptoms and 28% experienced hallucinations during the course of their dementia. No significant association was found either in frequency of genotype or allelic variation for either set of symptoms. Furthermore, the mean delusional and hallucinatory severity scores did not differ significantly among the three genotype groups. The lack of influence of the T102C polymorphism of the 5HT-2A receptor on the emergence of psychotic symptoms in AD contrasts with previous reports in other cohorts involving smaller numbers of subjects.

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)0.001
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 score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0020.000

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 head0.011
GPT teacher head0.282
Teacher spread0.270 · 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

Citations22
Published2006
Admission routes1
Has abstractyes

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Same venueAmerican Journal of Medical Genetics Part B Neuropsychiatric GeneticsSame topicSchizophrenia research and treatmentFrench-language works237,207