Antiretroviral Concentrations in Untimed Plasma Samples Predict Therapy Outcome in a Population with Advanced Disease
Bibliographic record
Abstract
This study was designed to examine the relationship between untimed antiretroviral concentrations measured in plasma samples collected for virus-load testing and response to highly active antiretroviral therapy. Plasma nonnucleoside reverse-transcriptase–inhibitor and protease-inhibitor concentrations were retrospectively mea-sured in all virus-load plasma samples collected during the first year of therapy, for 122 patients in British Columbia, Canada, who initiated therapy between August 1996 and September 1999 and who had CD4 counts!50 cells/mL. Drug levels were designated a priori as “low ” if the concentrations were below the published. A single low drug level measured shortly after initiation of therapy (median, 6 weeks) is commonC SDtrough (30%) and is predictive of both more-rapid immunological failure ( ) and failure to achieve virologicPp.06 success during the first year of therapy (). These results may reflect incomplete adherence, since aPp.01 strong association ( ) was found between low drug levels and an imperfect prescription-refill recordP!.001 (!95%). The use of a potent combination of antiretroviral drugs has led to dramatic reductions in the morbidity and mortality associated with human immunodeficiency vi-rus (HIV)–1 infection [1]. However, ∼20 % of therapy-naive patients fail to achieve adequate virological re-sponse during their first year of triple therapy, with
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".