Personalized Medicine and Tobacco-Related Health Disparities: Is There a Role for Genetics?
Bibliographic record
Abstract
Genetic testing has been proposed as a means to increase smoking cessation rates and thus reduce smoking prevalence. To understand how that might be practically possible, with appreciation of the current social context of tobacco use and dependence, we performed a contextual analysis of smoking-related genetics and smoking cessation. To provide added value, genetics would need to inform and improve existing interventions for smokers (including behavioral and pharmacological treatments). Pharmacogenetics offers the most promising potential, because it may improve the efficacy of medication-based smoking cessations strategies. All proven interventions for treating tobacco dependence, however, including simple cost-effective measures, such as quit lines and physician counseling, are underutilized. As tobacco use occurs disproportionately among disadvantaged populations, efforts to improve smokers' access to health care, and to the tools that are known to help them quit, represent the most promising approaches for reducing smoking prevalence within these groups. Similar considerations apply to other chronic diseases contributing to population-level health disparities. We conclude that although genetics offers increasing opportunities to tailor drug treatment, and may in some cases provide useful risk prediction, other methods of personalizing care are likely to yield greater benefit to populations experiencing health disparities related to tobacco use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".