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Record W2145708643 · doi:10.1177/0269881114548295

The effects of beliefs regarding drug assignment in experimental and field studies of nicotine delivery devices: A review

2014· review· en· W2145708643 on OpenAlexaff
Reuven Dar, Sean P. Barrett

Bibliographic record

VenueJournal of Psychopharmacology · 2014
Typereview
Languageen
FieldNeuroscience
TopicPain Management and Placebo Effect
Canadian institutionsDalhousie University
Fundersnot available
KeywordsNicotineCravingPlaceboDrugSmoking cessationNicotine replacement therapyPharmacologyMedicinePsychologyNicotine withdrawalAddictionPsychiatryAlternative medicine

Abstract

fetched live from OpenAlex

The placebo effect of a psychoactive drug can be defined as the effect of expecting the drug in the absence of its pharmacological actions. As nicotine is widely believed to be the primary factor driving cigarette smoking, smokers are likely to expect nicotine to alleviate craving and withdrawal. The present review examines the extent to which any observed effects of nicotine, and especially its craving- and withdrawal-reducing effects, can be attributed to placebo. We begin by reviewing studies that examined the placebo effects of nicotine in the laboratory and follow with a review of potential placebo effects that are typically not controlled in placebo-controlled studies of nicotine replacement therapy (NRT). In laboratory studies, nicotine instructions decrease tobacco smoking, craving and/or withdrawal, while nicotine-specific effects have not been consistently reported. In field trials of NRT, there is a general failure to assess smokers' beliefs regarding their drug assignment. This omission makes it difficult to unequivocally attribute findings of placebo-controlled NRT studies to the physiological effects of nicotine. In sum, our review indicates that the placebo effects of nicotine, and specifically nicotine content expectations, may account for many of the benefits associated with nicotine delivery devices in both laboratory and field studies.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.834
Threshold uncertainty score0.696

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.045
GPT teacher head0.413
Teacher spread0.368 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations13
Published2014
Admission routes1
Has abstractyes

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