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Record W4408589792 · doi:10.7554/elife.91933.2

Effects of nicotine compared to placebo gum on sensitivity to pain and mediating effects of peak alpha frequency

2025· preprint· en· W4408589792 on OpenAlexaff
Samantha K. Millard, Alan Chiang, Peter Humburg, Nahian Chowdhury, Raafay Rehan, Andrew J. Furman, Ali Mazaheri, Siobhan M. Schabrun, David A. Seminowicz

Bibliographic record

VenueeLife · 2025
Typepreprint
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsParkwood InstituteWestern University
Fundersnot available
KeywordsPlaceboNicotine gumAlpha (finance)NicotineSensitivity (control systems)MedicineAnesthesiaPsychologyPharmacologyInternal medicineClinical psychologyAlternative medicine

Abstract

fetched live from OpenAlex

Recent research has linked individual peak alpha frequency (PAF) to pain sensitivity, but whether PAF alterations can influence pain remains unclear. Our study investigated the effects of nicotine on pain sensitivity and whether pain changes are mediated by PAF changes. In a randomised, double-blind, placebo-controlled experiment, 62 healthy adults (18–44 years) received either 4 mg nicotine gum (n=29) or placebo (n=33). Resting state EEG and pain ratings during prolonged heat and pressure models were collected before and after nicotine intake. The nicotine group had a decrease in heat pain ratings compared to the placebo group and increased PAF speed across the scalp from pre to post-gum, driven by changes at central-parietal and right-frontal regions. However, mediation analysis did not support the notion that PAF changes mediate nicotine’s effects on pain sensitivity. While a growing body of literature supports a link between PAF and both acute and chronic pain, further work is needed to understand the mechanisms of this link.

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: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.008
GPT teacher head0.267
Teacher spread0.259 · 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 designRandomized trial
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

Citations0
Published2025
Admission routes1
Has abstractyes

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