Brief Report: Protease Inhibitors Versus Nonnucleoside Reverse Transcriptase Inhibitors and the Risk of Cancer Among People With HIV
Bibliographic record
Abstract
Background: The effect of initial antiretroviral therapy (ART) class on cancer risk in people with HIV (PWH) remains unclear. Setting: Cohort study of 36,322 PWH enrolled (1996-2014) in the North American AIDS Cohort Collaboration on Research and Design. Methods: We followed individuals from ART initiation (protease inhibitor [PI]-, non-nucleoside reverse transcriptase inhibitor [NNRTI]-, or integrase strand transfer inhibitor [INSTI]-based) until incident cancer, death, loss-to-follow-up, 12/31/2014, 85 months (intention-to-treat analyses [ITT]), or 30 months (per-protocol [PP] analyses). Cancers were grouped (non-mutually exclusive) as: any cancer, AIDS-defining cancers (ADC), non-AIDS-defining cancers (NADC), any infection-related cancer, and common individual cancer types. We estimated adjusted hazard ratios (aHR) comparing cancer risk by ART class using marginal structural models emulating ITT and PP trials. Results: We observed 17,004 PWH (954 cancers) with PI-based (median 6 years follow-up), 17,536 (770 cancers) with NNRTI-based (median 5 years follow-up) and 1,782 (29 cancers) with INSTI-based ART (median 2 years follow-up). Analyses with 85 months follow-up indicated no cancer risk differences. In truncated analyses, risk of ADCs (aHR 1.33; 95% CI 1.00, 1.77 [PP-analysis]) and NADCs (aHR 1.23; 95% CI 1.00, 1.51[ITT-analysis]) were higher comparing PIs vs. NNRTIs. Conclusions: Results with longer-term follow-up suggest being on a PI- versus NNRTI-based ART regimen does not affect cancer risk. We observed shorter-term associations that should be interpreted cautiously and warrant further study. Further research with longer duration of follow-up that can evaluate INSTIs, the current first-line recommended therapy, is needed to comprehensively characterize the association between ART class and cancer risk.
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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 | 0.002 | 0.014 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".