Clinical Utility of Testing Human Immunodeficiency Virus for Drug Resistance
Bibliographic record
Abstract
Human immunodeficiency virus (HIV) type 1 drug-resistance testing is quickly moving from the research laboratory to the clinic as data defining its utility as a prognostic indicator of response to therapy become available. In July 1998, a panel of the International AIDS Society-USA did not recommend the widespread application of resistance testing, but by May 2000 this panel endorsed and recommended the incorporation of resistance testing in patient-care management. Considerable data supporting the use of drug-resistance testing have now been published or presented at international conferences. These data strongly suggest that drug-resistance testing is of considerable value in many clinical settings. Prospective trials of resistance testing as a clinical management tool are still ongoing, and the long-term benefits still need to be evaluated. Nevertheless, early results from several studies showed a significantly better virological response when treatment regimens were based on resistance-testing data, rather than on the standard of care. HIV drug-resistance testing is also useful as a tool for new antiretroviral drug design and development, as well as for monitoring the spread of primary HIV drug resistance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".