Resistencia a drogas antirretrovirales en pacientes expuestos a terapia antirretroviral en Cali, Colombia, 2008-2010
Bibliographic record
Abstract
INTRODUCTION: Little has been published in Colombia on HIV drug resistance in patients taking antiretroviral treatment (ART). Currently, the Colombian guidelines do not recommend the use of genotypic antiretroviral resistance tests (GART) for treatment-naive patients or for those experiencing a first therapeutic failure. OBJECTIVE: To determine the frequency of relevant resistance mutations and the degree of susceptibility/ resistance of HIV to antiretroviral drugs (ARVs) in ART-experienced patients. MATERIALS AND METHODS: A non-random sample of 170 ART-experienced HIV patients with virologic failure and who underwent GART was recruited. A study of HIV drug resistance was carried out in two groups of patients: one group that underwent early GART and the other group that received late GART testing. RESULTS: The most frequent type of resistance affected the non-nucleoside class (76%). The late-GART group had higher risk of nucleoside analog and protease inhibitor drug resistance, a higher number of resistance mutations and more complex mutational profiles than the early-GART group. A high cross resistance level (30%) was found in the nucleoside analog class. The least affected medications were tenofovir and darunavir. CONCLUSIONS: Our results suggest that performing GART late is associated with levels of ARV resistance that could restrict the use of an important number of essential ARV in subsequent regimens. There is a need to revise the current recommendations to include GART prior to start of treatment and after the first virologic failure.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".