Bibliographic record
Abstract
Abstract Introduction Tobacco use disorder (TUD) is a major worldwide healthcare burden resulting in 7 million deaths annually. TUD has few approved cessation aids, all of which are associated a high rate of relapse within one year. Biomarkers of TUD severity, treatment response, and risk of relapse have high potential clinical utility to identify ideal responders and guide additional treatment resources. Methods A MEDLINE search was performed using the terms biomarkers, dihydroxyacetone phosphate, bilirubin, inositol, cotinine, adrenocorticotropic hormone, cortisol, pituitary-adrenal system, homovanillic acid, dopamine, pro-opiomelanocortin, lipids, lipid metabolism all cross-referenced with tobacco-use disorder . Results The search yielded 424 results, of which 57 met inclusion criteria. The most commonly studied biomarkers were those related to nicotine metabolism, the hypothalamic-pituitary-adrenal (HPA) axis, and cardiovascular (CVD) risk. Nicotine metabolism was most associated with severity of dependence and treatment response, where as HPA axis and CVD markers showed less robust associations with dependence and relapse risk. Conclusions Nicotine-metabolite ratio, cortisol, and atherogenicity markers appear to be the most promising lead biomarkers for further investigation, though the body of literature is still preliminary. Longitudinal, repeated-measures studies are required to determine the directionality of the observed associations and determine true predictive power of these biomarkers. Future studies should also endeavour to study populations with comorbid psychiatric disorders to determine differences in utility of certain biomarkers.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".