The effects of nicotine, denicotinized tobacco, and nicotine-containing tobacco on cigarette craving, withdrawal, and self-administration in male and female smokers
Bibliographic record
Abstract
The effects of the acute administration of nicotine [through nicotine inhalers (NI) and placebo inhalers (PI)], nicotine-containing tobacco (NT), and denicotinized tobacco (DT), on smokers' subjective responses and motivation to smoke were examined in 22 smokers (12 male, 10 female; 11 low dependent, 11 high dependent). During four randomized blinded sessions, participants self-administered NI, PI, NT, or DT, and assessed their effects using Visual Analogue Scales and the Brief Questionnaire of Smoking Urges. They could then self-administer their preferred brand of cigarettes using a progressive ratio task. NT and DT were each associated with increased satisfaction and relaxation as well as decreased craving relative to the inhalers and NT increased ratings of stimulation relative to each of the other products. Both NT and DT delayed the onset of preferred tobacco self-administration relative to NI and PI but only NT reduced the total amount self-administered. Sex differences were evident in the effects of DT on withdrawal-related cravings with women experiencing greater DT-induced craving relief than men. Findings suggest that DT is effective in acutely reducing many smoking abstinence symptoms, especially in women, but a combination of nicotine and non-nicotine tobacco ingredients may be necessary to suppress smoking behavior.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".