Smoking cue reactivity and smoking motives in patients with early phase psychotic disorder
Bibliographic record
Abstract
BACKGROUND: Rates of tobacco smoking in individuals with psychosis are high and are unfortunately accompanied by low rates of successful smoking cessation. Exposure to smoking-related cues is shown to reliably enhance cigarette cravings in dependent people who smoke recruited from the general population. Recent data suggests that smoking-related cues do not affect people who smoke diagnosed with established schizophrenia to the same degree as non-psychotic populations. However, less is known about how individuals in the early phase of psychotic illness react to these cues. In addition, it is unknown whether the smoking motives of individuals with early phase psychosis (EPP) differ from those of non-psychotic individuals who smoke (NPS). STUDY DESIGN: The present study compared cue-elicited cigarette cravings of EPP (n = 21, mean(sd) age27.6(7.8) years) to NPS (n = 40) in response to a smoking cue-reactivity paradigm and additionally, compared general self-reported motives for smoking across these groups. STUDY RESULTS: Similar to those with established psychosis, EPP individuals were less sensitive to the cue-reactivity paradigm, suggesting that individuals in early phase of illness may not be affected by tobacco cues to the same degree as NPS. Further, comparison of smoking motives suggests no differences between the reasons for smoking reported by individuals with EPP and NPS. CONCLUSIONS: These findings have implications for future studies aimed at identifying the source of the wide discrepancy in rates of tobacco smoking and smoking cessation across these two populations, and for the development of effective smoking cessation interventions for people who smoke with EPP.
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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 | 0.002 |
| 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 | 0.001 |
| 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".