Evidence of subgroups in smokers as revealed in clinical measures and evaluated by neuroimaging data: a preliminary study
Bibliographic record
Abstract
To date, fractionation of the nicotine addiction phenotype has been limited to that based primarily on characteristics of cigarette use, although it is widely appreciated that a variety of individual factors are associated with tobacco use disorder. Identifying subtypes of tobacco use disorder based on such factors may lead to better understanding of potential treatment targets, individualize treatments and improve outcomes. In this preliminary study, to identify potential subgroups, we applied hierarchical clustering to a broad range of assessments measuring personality, IQ and psychiatric symptoms, as well as various environmental and experiential characteristics from 102 otherwise healthy cigarette smokers. The identified subgroups were further compared on various resting-state fMRI measures from a subset (N = 65) of individuals who also underwent resting-state fMRI scanning. The clustering dendrogram indicated that smokers can be divided into three subgroups. Each subgroup had unique clinical assessment characteristics. The division yielded imaging differences between subgroups in the supplementary motor area/middle cingulate cortex and the cuneus. Regression analyses showed that amplitude of low frequency fluctuations in the supplementary motor area/middle cingulate cortex differed between groups and were negatively correlated with the Toronto Alexithymia Scale subscale Difficulty Describing Feelings.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".