Predictive value of immune parameters before treatment interruption (TI) for CD4 <sup>+</sup> T-cell count change during TI in HIV infection
Bibliographic record
Abstract
BACKGROUND: Despite the contraindications, stopping treatment for HIV infection continues to be a common practice. Understanding whether T-cell proliferative capacity and phenotypic markers before treatment interruption (TI) can predict CD4+ T-cell count change and nadir during TI would be clinically useful. METHODS: This retrospective study included 27 HIV-infected patients in the chronic phase of infection while on combination antiretroviral therapy (cART) who underwent a TI. Peripheral blood mononuclear cells from a baseline pre-TI time point were screened for T-cell proliferation to cytomegalovirus (CMV) lysate, an HIV Gag p55 peptide pool as well as positive and negative control stimuli. CD28 and CD57 expression on CD4+ and CD8+ T-cells were measured. RESULTS: Baseline viral load, CD4+ T-cell count, pre-cART nadir CD4+ T-cell and percentage CD4+CD28+ T-cells were all predictive of the lowest CD4+ T-cell count during TI (Spearman's correlation P<0.05 for all analyses). In addition, CD4+ and CD8+ T-cells proliferation to CMV lysate, baseline CD4+ T-cell count and percentage CD4+CD57+ T-cells correlated negatively with CD4+ T-cell decrease during TI (Spearman's correlation P<0.05 for all analyses). CONCLUSIONS: In treated chronic HIV-infected patients, pre-TI immune parameters are potential predictors for both the nadir CD4+ T-cell count and CD4+ T-cell count decrease during TI.
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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".