Protease inhibitors exposure is not related to lung cancer risk in HIV smoker patients
Bibliographic record
Abstract
OBJECTIVE: We aimed at assessing in persons living with HIV with a smoking history an association between lung cancer risk and protease inhibitors exposure, especially ritonavir. DESIGN: A nested case-control study was conducted within the ANRS CO4 FHDH, CO3 Aquitaine and Tenon's Hospital Cohorts. METHODS: Cases and controls were eligible if they were ex-smokers or current smokers at the index date, and had a CD4 cell count reported in the year preceding the index date. Cases were incident cases of lung cancer diagnosed between 1 January 2000 and 31 December 2011. All cancer cases were validated and histological types identified when available. Three controls were randomly selected by incidence density sampling using calendar time as the time axis, with individual matching on cohort, age (± 5 years), route of HIV acquisition, sex and hospital. Analyses were performed using conditional logistic regression adjusted for nadir CD4 cell count and smoking status. Ritonavir and protease inhibitors exposures were represented in separate models using categorical variables (never exposed, ever exposed). Several sensitivity analyses were performed. RESULTS: This study performed in 1447 persons living with HIV with a smoking history (383 lung cancer cases and 1064 control patients) did not evidence any association between lung cancer risk and protease inhibitors exposure including ritonavir. CONCLUSION: These results suggest that the risk of lung cancer is not influenced by pharmacologically induced P450 cytochrome protease inhibitors inhibition among smokers or ex-smokers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".