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Record W2518513729 · doi:10.1136/eb-2016-102474

Lack of clinically useful response predictors for treating aggression and agitation in Alzheimer's disease with citalopram

2016· letter· en· W2518513729 on OpenAlexaff
Nathan Herrmann

Bibliographic record

VenueEvidence-Based Mental Health · 2016
Typeletter
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsUniversity of TorontoHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsCitalopramAggressionDiseaseMedicinePsychologyInternal medicineClinical psychologyNeurosciencePsychiatryAntidepressantHippocampus

Abstract

fetched live from OpenAlex

ABSTRACT FROM: Schneider LS, Frangakis, C, Drye LT, et al . Heterogeneity of treatment response to citalopram for patients with Alzheimer's disease with aggression or agitation: the CitAD randomized clinical trial. Am J Psychiatry 2016;173:465–72. Neuropsychiatric symptoms (NPS) associated with Alzheimer's disease (AD) include depression, apathy, hallucinations, delusions, wandering and anxiety. Of all the NPS, agitation and aggression are among the most problematic and are associated with increased risk of mortality, earlier institutionalisation, increased cost of care and markedly increased caregiver burden. Over two dozen randomised placebo-controlled trials with a variety of atypical antipsychotics have demonstrated modest efficacy for agitation and aggression, with and without psychotic symptoms. Unfortunately, the use of atypical antipsychotics in patients with dementia is associated with an increased risk of cerebrovascular adverse events and mortality.1 As a result, clinical practice guidelines recommend their use only after environmental and behavioural interventions have been tried, and only when the behaviours represent a risk for harm to the patient and others.2 Because of this unfavourable risk/benefit ratio, …

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.464
Threshold uncertainty score0.876

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.123
GPT teacher head0.422
Teacher spread0.299 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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