Increased CD4 : CD8 ratio normalization with implementation of current ART management guidelines
Bibliographic record
Abstract
OBJECTIVES: To determine the time to CD4 : CD8 ratio normalization among Canadian adults living with HIV in the modern ART era. To identify characteristics associated with ratio normalization. PATIENTS AND METHODS: Retrospective analysis of the Canadian Observational Cohort (CANOC), an interprovincial cohort of ART-naive adults living with HIV, recruited from 11 treatment centres across Canada. We studied participants initiating ART between 1 January 2011 and 31 December 2016 with baseline CD4 : CD8 ratio <1.0 and ≥2 follow-up measurements. Normalization was defined as two consecutive CD4 : CD8 ratios ≥1.0. Kaplan-Meier estimates and log-rank tests described time to normalization. Univariable and multivariable proportional hazards (PH) models identified factors associated with ratio normalization. RESULTS: Among 3218 participants, 909 (28%) normalized during a median 2.6 years of follow-up. Participants with higher baseline CD4+ T-cell count were more likely to achieve normalization; the probability of normalization by 5 years was 0.68 (95% CI 0.62-0.74) for those with baseline CD4+ T-cell count >500 cells/mm3 compared with 0.16 (95% CI 0.11-0.21) for those with ≤200 cells/mm3 (P < 0.0001). In a multivariable PH model, baseline CD4+ T-cell count was associated with a higher likelihood of achieving ratio normalization (adjusted HR = 1.5, 95% CI 1.5-1.6 per 100 cells/mm3, P < 0.0001). After adjusting for baseline characteristics, time-dependent ART class was not associated with ratio normalization. CONCLUSIONS: Early ART initiation, at higher baseline CD4+ T-cell counts, has the greatest impact on CD4 : CD8 ratio normalization. Our study supports current treatment guidelines recommending immediate ART start, with no difference in ratio normalization observed based on ART class used.
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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 | 0.012 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".