Setting Research Priorities to Reduce Almost One Million Deaths from Birth Asphyxia by 2015
Bibliographic record
Abstract
Background World Health Organization (WHO) guidelines for monitoring HIV-infected individuals taking combination antiretroviral therapy (cART) in resource-limited settings recommend using CD4þ T cell (CD4) count changes to monitor treatment effectiveness. In practice, however, falling CD4 counts are a consequence, rather than a cause, of virologic failure. Adherence lapses precede virologic failure and, unlike CD4 counts, data on adherence are immediately available to all clinics dispensing cART. However, the accuracy of adherence assessments for predicting future or detecting current virologic failure has not been determined. The goal of this study therefore was to determine the accuracy of adherence assessments for predicting and detecting virologic failure and to compare the accuracy of adherence-based monitoring approaches with approaches monitoring CD4 count changes. Methodology and Findings We conducted an observational cohort study among 1,982 of 4,984 (40%) HIV-infected adults initiating non-nucleoside reverse transcriptase inhibitor-based cART in the Aid for AIDS Disease Management Program, which serves nine countries in southern Africa. Pharmacy refill adherence was calculated as the number of months of cART claims submitted divided by the number of complete months between cART initiation and the last refill prior to the endpoint of interest, expressed as a percentage......Conclusions Pharmacy refill adherence assessments were as accurate as CD4 counts for detecting current virologic failure in this cohort of patients on cART and have the potential to predict virologic failure before it occurs. Approaches to cART scale-up in resource-limited settings should include an adherence-based monitoring approach.
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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 | 0.175 | 0.199 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".