Effects of Tobacco Smoking Status on Verbal Learning and Memory in Patients With Schizophrenia and Non‐Psychiatric Controls
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Patients with schizophrenia have higher rates of tobacco smoking compared to the general population. Moreover, these patients have deficits in cognition, including verbal learning and memory. However, it is not clear whether smoking status alters verbal learning and memory in schizophrenia. We examined the effects of smoking abstinence and reinstatement on verbal learning and memory in people with schizophrenia and nonpsychiatric controls and other cognitive domains as exploratory. METHODS: Smoking participants (N = 28; 14 schizophrenia smokers; 14 nonpsychiatric smokers) were studied under smoking satiated, overnight abstinence and smoking reinstatement conditions. Nonsmokers ( n = 30; 15 schizophrenia nonsmokers; 15 nonpsychiatric nonsmokers) were also studied. A comprehensive cognitive battery was administered including verbal learning and memory using the Hopkins Verbal Learning Test-Revised (HVLT-R). RESULTS: A 2 (diagnosis) × 2 (smoking status) repeated measures analysis of variance with time (session) as the within-subjects factor and diagnosis and smoking status as the between-subject factors was performed for HVLT-R and other cognitive outcomes. Smoking abstinence produced a decline in verbal memory of the HVLT discrimination index in smokers with schizophrenia that was partially revised by reinstatement, although trends for other HVLT measures were not statistically significant. CONCLUSIONS AND SCIENTIFIC SIGNIFICANCE: Acute cigarette smoking and abstinence may selectively alter verbal learning and memory deficits in smokers with schizophrenia compared to nonpsychiatric smoking controls and nonsmokers, but additional studies are needed to confirm the preliminary findings in this small sample. (Am J Addict 2019;00:1-9).
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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".