Bibliographic record
Abstract
Smoking is a complex behavior influenced by environ-mental and genetic factors. A number of twin studies demonstrate genetic influences on multiple aspects of smoking including initiation, dependence, continuation, amount consumed, and cessation. It is likely that this genetic risk involves a complex interaction between multiple genes in different biologic pathways. These may include genes involved in the general aspect of dependence (eg, genes within the dopaminergic system) and those more specific to nicotine dependence (eg, genes encoding nicotinic acetylcholine receptors and nicotine-metabolizing enzymes). Genome-wide linkage analyses and gene association studies provide approaches for identifying these genes. This review provides a brief highlight of data from some of the candidate gene association studies and discusses some of the discrepancies found in the literature. In addition, the authors have reviewed some recent studies examining how genetic variations in drug-metabolizing enzymes and drug targets can influence the response to treatment with drugs such as bupropion and nicotine.These latter studies provide the potential for maximizing treatment efficacy by individualizing the type, dosage, and duration of the treatment based on an individual's genotype/phenotype. Significant advances have been made in understanding the genetic contribution to smoking behaviors. These and future studies will provide potential directions for novel prevention and treatment strategies.
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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) | 0.001 | 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 | 0.001 | 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".