Rapid CD4 decline prior to antiretroviral therapy predicts subsequent failure to reconstitute despite HIV viral suppression
Bibliographic record
Abstract
within one year of viral load suppression. Age and nadir CD4 cell counts are known risk factors associated with immune reconstitution failure. We chose controls (Patients with immune reconstitution success) of similar age and CD4 nadir cell with cases (Patients with immune reconstitution failure). We explored the potential effects of gender, HLA type, presence of co-infection, ethnicity, ART type, and rate of pre-treatment CD4 decline among cases and controls. Of more than 550 patients followed by our HIV clinic, 42 individuals met our definition of immune reconstitution failure and they were assigned to the cases group. 31 patients, comprising a range of ages and CD4 nadirs similar to those of the cases, were assigned to the control group. Our primary analysis was a regression model, predicting post-ART change in CD4 over time. After controlling for age and nadir CD4 cell counts, the only potential predictor that appears consistently associated with the rate of post-ART rise in CD4 over time in our cohort, regardless of the other variables that we have controlled for, is the rate of decline in CD4 pre-ART initiation. Several factors have been variably correlated with immune reconstitution failure of CD4 T cell count. Age and low CD4 nadir are factors previously shown to correlate with immune reconstitution failure; and we have controlled for them in our study. Another possible predictor is the rate of decline in CD4 pre-ART, which can serve as an additional marker of reconstitution failure and necessitate prioritizing individuals to ART initiation or identification of a subset of individuals that may be targeted for future adjunct strategies to improve immune recovery.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".