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Record W6964545150 · doi:10.25934/pr00009081

Antidepressant effects of bupropion, varenicline, nicotine replacement therapy, and placebo in tobacco users.

2023· dataset· en· W6964545150 on OpenAlexaffabout

Bibliographic record

VenueVivli · 2023
Typedataset
Languageen
Field
Topic
Canadian institutionsMcGill University
Fundersnot available
KeywordsVareniclineNicotineCravingAntidepressantPlaceboSmoking cessationBupropionMoodNicotine replacement therapy

Abstract

fetched live from OpenAlex

Major depressive disorder (MDD) is one of the most common psychiatric conditions, affecting around 20.6% of adults in the United States. It is characterized by a persistent low mood and/or a loss of interest in normally pleasurable activities. MDD can also lead to changes in appetite, sleep difficulties, fatigue, trouble concentrating, feelings of guilt, and thoughts of suicide. Additionally, people with depression often smoke cigarettes at higher rates and have a greater risk of becoming dependent on nicotine. The EAGLES trial was the largest randomized clinical trial to date exploring smoking cessation treatments for people with psychiatric conditions, including MDD. The trial evaluated the comparative safety and efficacy of varenicline, bupropion, and nicotine replacement therapies. Varenicline (Champix/Chantix) is a non-nicotine-based medication used for smoking cessation. It provides relief from craving and withdrawal symptoms and diminishes the pleasurable sensation derived from smoking. Bupropion (Wellbutrin, Zyban) is an ‘atypical’ antidepressant that is also used for smoking cessation. It is known to act on two neurotransmitters called noradrenaline and dopamine, but its mechanism of action is not fully understood. Nicotine replacement therapies are alternative sources of nicotine (e.g. gum, nasal sprays, transdermal patches, sublingual tablets) that are used to reduce cravings and ease withdrawal symptoms. These three treatments were found to be well-tolerated and effective for helping the EAGLES trial participants quit smoking. In addition to the main analyses, several sub-analyses have been conducted, focusing on how well the treatments worked within specific groups of participants with different psychiatric conditions, most recently in MDD. Our project aims to build upon these findings by investigating the differential antidepressant effects of these three treatments compared to a placebo (any treatment that has no active properties, such as a sugar pill). Based on the already-published EAGLES trial data, we hypothesize that none of the trial's treatments will show significantly better antidepressant effects than placebo. This finding would be significant, since bupropion is an evidenced-based treatment for depression itself. To test our main hypothesis, we will use a statistical model (called “Mixed Effects Model”) that can account for individual variations in depression outcomes over time while also establishing whether or not there are differences between the three treatment groups compared to the placebo group. Our results will be valuable for physicians and researchers aiming to better understand the antidepressant effects of bupropion, in relation to other evidence-based treatments for tobacco use disorder, in a sample of smokers. We plan to publish our findings in a peer-reviewed scientific article and present any significant results at relevant academic conferences, such as the Canadian Psychiatric Association annual meeting.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.020
GPT teacher head0.282
Teacher spread0.262 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2023
Admission routes2
Has abstractyes

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