Smoking predicts brain atrophy in 10,134 healthy individuals and is potentially influenced by body mass index
Bibliographic record
Abstract
Abstract Cigarette smoking is a risk factor for Alzheimer’s and vascular dementia, but its impact on brain volume loss, a neurodegeneration biomarker on MRI, is unclear. In total, 10,134 participants from 4 sites were scanned with a whole-body 1.5 T MRI protocol with separate dedicated structural neuroimaging with 3D T1 MPRAGE sequences. Smokers versus non-smokers were compared by gray and white matter volumes normalized to total intracranial volume using a two-tailed t-test. Smokers had lower normalized gray (t = −7.806e+00, p = 6.508e-15) and white matter volumes (t = −7.374e + 00, p = 1.791e-13) compared to non-smokers. Adjusting for age, sex, study site, BMI, and multiple comparisons, higher pack years of smoking predicted volume loss in such regions as total gray matter volume, total white matter volume, temporal lobe, parietal lobe, hippocampus, precuneus, and posterior cingulate. The inclusion and exclusion of BMI from the model suggested an influence of this variable.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".