Cancer burden attributable to cigarette smoking among HIV-infected people in North America
Bibliographic record
Abstract
OBJECTIVE: With combination-antiretroviral therapy, HIV-infected individuals live longer with an elevated burden of cancer. Given the high prevalence of smoking among HIV-infected populations, we examined the risk of incident cancers attributable to ever smoking cigarettes. DESIGN: Observational cohort of HIV-infected participants with 270 136 person-years of follow-up in the North American AIDS Cohort Collaboration on Research and Design consortium. Among 52 441 participants, 2306 were diagnosed with cancer during 2000-2015. MAIN OUTCOME MEASURES: Estimated hazard ratios and population-attributable fractions (PAF) associated with ever cigarette smoking for all cancers combined, smoking-related cancers, and cancers that were not attributed to smoking. RESULTS: People with cancer were more frequently ever smokers (79%) compared with people without cancer (73%). Adjusting for demographic and clinical factors, cigarette smoking was associated with increased risk of cancer overall [hazard ratios = 1.33 (95% confidence interval: 1.18-1.49)]; smoking-related cancers [hazard ratios = 2.31 (1.80-2.98)]; lung cancer [hazard ratios = 17.80 (5.60-56.63)]; but not nonsmoking-related cancers [hazard ratios = 1.12 (0.98-1.28)]. Adjusted PAFs associated with ever cigarette smoking were as follows: all cancers combined, PAF = 19% (95% confidence interval: 13-25%); smoking-related cancers, PAF = 50% (39-59%); lung cancer, PAF = 94% (82-98%); and nonsmoking-related cancers, PAF = 9% (1-16%). CONCLUSION: Among HIV-infected persons, approximately one-fifth of all incident cancer, including half of smoking-related cancer, and 94% of lung cancer diagnoses could potentially be prevented by eliminating cigarette smoking. Cigarette smoking could contribute to some cancers that were classified as nonsmoking-related cancers in this report. Enhanced smoking cessation efforts targeted to HIV-infected individuals are needed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".