Smoking and tardive dyskinesia: lack of involvement of the <i>CYP1A2</i> gene
Bibliographic record
Abstract
Objective: To establish if there is an association between cigarette smoking and tardive dyskinesia (TD) in patients with schizophrenia and to evaluate the role of the CYP1A2 polymorphism in TD in patients of Chinese descent. Method: Two-hundred and ninety-one patients diagnosed with schizophrenia according to DSM-IV criteria were included in the study. Dyskinesia was assessed by the Abnormal Involuntary Movement Scale and TD by the criteria of Schooler and Kane. Demographic and clinical data and information on smoking habits were collected, and patients of Chinese descent with a well established smoking history were subsequently genotyped for CYP1A2. Results: Forty-three (41.3%) of the 104 patients with a history of smoking and 52 (27.8%) of the 187 non-smokers were diagnosed with TD. The prevalence of TD was significantly higher among smokers than non-smokers (χ2 = 5.57, p = 0.018). Logistic regression using TD as the dependent variable revealed smokers to be at a significantly higher risk for TD ( p < 0.005). Genotyping of smokers of Chinese descent for CYP1A2 polymorphism revealed no significant differences in the genotypic or allelic distribution between those with and without TD. Conclusions: Consistent with other studies, the prevalence of TD was significantly higher among smokers than non-smokers; however, we did not find an association between the C→A genetic polymorphism of CYP1A2 and TD.
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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) | 0.002 | 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".