Performance of Immunologic Responses in Predicting Viral Load Suppression
Bibliographic record
Abstract
BACKGROUND: World Health Organization (WHO) guidelines for the use of antiretroviral therapy (ART) in resource-limited settings state that CD4 cell counts may be used to indicate when ART regimens should be changed because of treatment failure. The performance of immunologic monitoring for this purpose has not been evaluated, however. METHODS: Participants aged > or =18 years from the British Columbia HIV/AIDS Drug Treatment Program who had CD4 cell counts < or =200 cells/microL or an AIDS diagnosis at baseline had CD4 cell counts measured at 6 and 12 months after treatment initiation. Logistic regression analysis was used to calculate sensitivity, specificity, and positive and negative predictive values for immunologic responses in terms of predicting failure to achieve 2 viral load measurements < 500 copies/mL within 1 year. RESULTS: Viral load suppression occurred in 674 (60%) of 1125 subjects. Using no increase in CD4 cell counts at 6 months as a definition of treatment failure had a sensitivity of 34%, specificity of 94%, positive predictive value of 75%, and negative predictive value of 71% for predicting failure to achieve virologic suppression. Using 12-month CD4 cell count values, the measurements were 35%, 95%, 79%, and 73%, respectively. CONCLUSION: Immunologic criteria to predict which patients have not achieved virologic suppression results in significant misclassification of therapeutic responses.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".