Role of Therapeutic Drug Monitoring for Protease Inhibitors
Bibliographic record
Abstract
OBJECTIVE: To review the role of therapeutic drug monitoring (TDM) for protease inhibitors in the treatment of HIV infection. DATA SOURCES: Primary articles were identified using MEDLINE (1966-October 2000), EMBASE (1987-October 2000), AIDSLINE (1980-October 2000), Current Contents, PubMed, and Medscape. Further articles were identified from bibliographic review of primary articles and review papers. Abstracts presented at the World AIDS Conference, Interscience Conference on Antimicrobial Agents and Chemotherapy, and Conference on Retroviruses and Opportunistic Infections were also identified from 1997 to 2000. STUDY SELECTION AND DATA EXTRACTION: All English-language, prospective clinical trials, as well as selected retrospective studies and case series, pertaining to therapeutic drug monitoring of protease inhibitors were included. DATA SYNTHESIS: A number of clinical studies have found a good relationship between concentration and pharmacologic response and/or toxicity as well as wide interpatient variability in the pharmacokinetics of protease inhibitors. There also is some preliminary evidence of the usefulness of plasma drug concentrations to guide dosage adjustments of protease inhibitors in patients with liver dysfunction. Furthermore, there is preliminary evidence of a relationship between drug concentrations and resistance. CONCLUSIONS: A number of clinical studies support the usefulness of TDM of protease inhibitors. However, before TDM can be of the most value, further evaluation requires simplified and standardized assays to be performed routinely by clinical laboratories; determination of the appropriate target concentration and therapeutic range, as well as the best predictor of pharmacologic response; and refined interpretation of plasma drug concentrations. Randomized, controlled clinical trials of patient outcomes are needed to assess the clinical utility of TDM for protease inhibitors.
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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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".