CD4/CD8 Ratio Outcome According to the Class of the Third Active Drug in Antiretroviral Therapy Regimens: Results From the Quebec Human Immunodeficiency Virus Cohort Study
Bibliographic record
Abstract
BACKGROUND: The impact of different therapeutic classes of drugs in antiretroviral therapy (ART) regimens on the CD4/CD8 ratio is not well documented in people treated for HIV. The objective of this study was to analyze the long-term effect of exposure to integrase strand transfer inhibitor (INSTI) on CD4/CD8 ratio compared with nonnucleoside reverse transcriptase inhibitor (NNRTI) or protease inhibitor (PI) among ART-treated persons with HIV (PWH). METHODS: Data from the Quebec HIV Cohort collected from 31 August 2017 were used. Our analysis included all patients in the cohort who received a first or subsequent ART regimen composed of 2 nucleoside reverse transcriptase inhibitors (NRTIs) and a third active drug of a different class (NNRTI, PI, or INSTI) for at least 16 weeks. Marginal structural Cox models were constructed to estimate the effect of different therapeutic classes on the CD4/CD8 ratio outcome. RESULTS: Among the 3907 eligible patients, 972 (24.9%), 1996 (51.1%), and 939 (24.0%) were exposed to an ART regimen whose third active agent was an NNRTI, PI, or INSTI, respectively. The total follow-up time was 13 640.24 person-years. The weighted hazard ratio for the association between the third active class and CD4/CD8 ratio ≥1 was .56 (95% confidence interval [CI]: .48-.65) for patients exposed to NNRTI + 2 NRTIs and .41 (95% CI: .35-.47) for those exposed to PI + 2 NRTIs, compared with those exposed INSTI + 2 NRTIs. CONCLUSIONS: For people treated for HIV, INSTI-based ART appears to be associated with a higher CD4/CD8 ratio than NNRTI and PI-based ART.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".