Effects of Tobacco Smoking on Neuropsychological Function in Schizophrenia in Comparison to Other Psychiatric Disorders and Non‐psychiatric Controls
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 it