Significant Reduction in HIV Virologic Failure During a 15-Year Period in a Setting With Free Healthcare Access
Bibliographic record
Abstract
BACKGROUND: Calendar trends in virologic failure (VF) among human immunodeficiency virus (HIV)-infected patients can help to evaluate the performance of healthcare systems and the need for new antiretroviral therapy (ART). We examined the time trend in the rate of VF beyond 6 months of ART between 1997 and 2011 in France. METHODS: We included patients from the French Hospital Database on HIV who received at least 6 months of ART. VF was defined as 2 consecutive plasma HIV-RNA values >500 copies/mL or as 1 value >500 copies/mL followed by a treatment switch. We adjusted for patients' characteristics by fitting a multivariable generalized estimating equation logistic regression model with an exchangeable covariance matrix. RESULTS: A total of 81 738 patients were enrolled, and median follow-up was 112.4 months. Median CD4 count was 333 cells/µL, and 23% of patients had HIV infection classified as Centers for Disease Control and Prevention stage C. Overall, 29.3% of patients received single/dual-drug ART initially, and 45.4% of patients experienced at least 1 episode of VF during follow-up. The percentage of patients with VF fell from 61.5% in 1997-1998 to 9.7% in 2009-2011 (P < .0001). Factors associated with the lower frequency of VF were recent calendar period, a higher contemporary CD4 cell count, and first-line regimens based on nonnucleoside reverse transcriptase inhibitors or integrase inhibitors. CONCLUSIONS: The proportion of HIV-infected patients experiencing VF during routine care fell markedly between 1997 and 2009-2011, to only 9.7%. This was attributed to the advent of fully active and better-tolerated antiretroviral drugs, and to national guidelines recommending rapid management of VF after mid-2000.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".