Nicotine dependence is associated with depression and childhood trauma in smokers with schizophrenia. Results from the Face-SZ dataset
Bibliographic record
Abstract
Introduction In a perspective of personalized care for smoking cessation, a better clinical characterization of smokers with schizophrenia (SZ) is needed. The objective of this study was to determine the clinical characteristics of SZ smokers with severe nicotine (NIC) dependence. Methods Two hundred and forty stabilized community-dwelling SZ smokers (mean age = 31.9 years, 80.4% male gender) were consecutively included in the network of the FondaMental Expert Centers for schizophrenia and assessed with validated scales. Severe NIC dependence was defined by a Fagerstrom questionnaire score ≥7. Major depression was defined by a Calgary score ≥6. Childhood trauma was self-reported by the Childhood Trauma Questionnaire score (CTQ). Ongoing psychotropic treatment was recorded. Results Severe NIC dependence was identified in 83 subjects (34.6%), major depression in 60 (26.3%). 44 (22.3%) subjects were treated by antidepressants. In a multivariate model, severe NIC dependence remained associated with major depression (OR = 3.155, P = 0.006), male gender (OR = 4.479, P = 0.009) and more slightly with childhood trauma (OR = 1.032, P = 0.044), independently of socio-demographic characteristics, psychotic symptoms severity, psychotropic treatments and alcohol disorder. Conclusion NIC dependence was independently and strongly associated with respectively major depression and male gender in schizophrenia, and only slightly with history of childhood trauma. Based on these results, the care of both nicotine dependence and depression should be evaluated for an effective smoking cessation intervention in schizophrenia. Bupropion, an antidepressant that has been found as the potential most effective strategy for tobacco cessation in schizophrenia to date, may be particularly relevant in male SZ smokers with comorbid major depression. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".