Tobacco Smoking and Pack-Years Are Associated With Frailty Among People With HIV
Bibliographic record
Abstract
BACKGROUND: Tobacco smoking increases frailty risk among the general population and is common among people with HIV (PWH) who experience higher rates of frailty at younger ages than the general population. METHODS: We identified 8608 PWH across 6 Centers for AIDS Research Network of Integrated Clinical Systems sites who completed ≥2 patient-reported outcome assessments, including a frailty phenotype measuring unintentional weight loss, poor mobility, fatigue, and inactivity, and scored 0-4. Smoking was measured as baseline pack-years and time-updated never, former, or current use with cigarettes/day. We used Cox models to associate smoking with risk of incident frailty (score ≥3) and deterioration (frailty score increase by ≥2 points), adjusted for demographics, antiretroviral medication, and time-updated CD4 count. RESULTS: The mean follow-up of PWH was 5.3 years (median: 5.0), the mean age at baseline was 45 years, 15% were female, and 52% were non-White. At baseline, 60% reported current or former smoking. Current (HR: 1.79; 95% confidence interval: 1.54 to 2.08) and former (HR: 1.31; 95% confidence interval: 1.12 to 1.53) smoking were associated with higher incident frailty risk, as were higher pack-years. Current smoking (among younger PWH) and pack-years, but not former smoking, were associated with higher risk of deterioration. CONCLUSIONS: Among PWH, smoking status and duration are associated with incident and worsening frailty.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".