Effects of Tobacco Smoking on Neuropsychological Function in Schizophrenia in Comparison to Other Psychiatric Disorders and Non‐psychiatric Controls
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Compared to the general population cigarette smoking prevalence is elevated in psychiatric disorders such as schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD). These disorders are also associated with neurocognitive impairments. Cigarette smoking is associated with improved cognition in SZ. The effects of smoking on cognition in BD and MDD are less well studied. METHODS: We used a cross-sectional design to study neuropsychological performance in these disorders as a function of smoking status. Subjects (N = 108) were SZ smokers (n = 32), SZ non-smokers (n = 15), BD smokers (n = 10), BD non-smokers (n = 6), MDD smokers (n = 6), MDD non-smokers (n = 10), control smokers (n = 12), and control non-smokers (n = 17). Participants completed a neuropsychological battery; smokers were non-deprived. RESULTS: SZ subjects performed significantly worse than controls in select domains, while BD and MDD subjects did not differ from controls. Three verbal memory outcomes were improved in SZ smokers compared with non-smokers; smoking status did not alter performance in BD or MDD. CONCLUSIONS AND SCIENTIFIC SIGNIFICANCE: These data suggest that smoking is associated with neurocognitive improvements in SZ, but not BD or MDD. Our data may suggest specificity of cigarette-smoking modulation of neurocognitive deficits in SZ.
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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 | 0.001 | 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".