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Record W1949985974

The role of selective serotonin reuptake inhibitors in reducing alcohol consumption.

2001· article· en· W1949985974 on OpenAlex
C. A. Naranjo, Della Knoke

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePubMed · 2001
Typearticle
Languageen
FieldNeuroscience
TopicNeurotransmitter Receptor Influence on Behavior
Canadian institutionsSunnybrook Health Science Centre
Fundersnot available
KeywordsFluvoxamineCitalopramFluoxetineSerotonergicSerotonin Uptake InhibitorsReuptake inhibitorAlcoholPsychologySerotoninAlcohol use disorderSerotonin reuptake inhibitorPsychiatryPharmacologyMedicineAntidepressantInternal medicineAnxietyBiology
DOInot available

Abstract

fetched live from OpenAlex

Preclinical and clinical studies demonstrated an inverse relationship between serotonergic activity and alcohol consumption. The selective serotonin reuptake inhibitors (SSRIs) fluoxetine, citalopram, and fluvoxamine have subsequently been examined for their ability to reduce alcohol consumption in alcoholic subjects. Interindividual variability in response to SSRIs is large, with reductions in alcohol consumption ranging from 10% to more than 70%. Several factors, including gender, alcoholic subtype, and extent of drinking, appear to affect the treatment efficacy of the SSRIs. A significant challenge for researchers is to identify the subject variables that predict treatment response, providing a basis for guiding alcohol-dependent individuals to the treatment that is most likely to be effective for them. This article reviews the available clinical studies, discusses possible mechanisms of action for the SSRIs, and describes a model for predicting treatment responses in alcoholic 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.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.213
Threshold uncertainty score0.475

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.043
GPT teacher head0.271
Teacher spread0.228 · 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