Which antiretrovirals should be prescribed as first-line treatments? Changes over the past 10 years in France
Bibliographic record
Abstract
Objective To describe the changes in first-line antiretroviral (ART) regimens in France between 2005 and 2015 and patients’ characteristics related to the use of protease inhibitors in 2015. Methods We extracted all patients starting ART between 2005 and 2015 from a large prospective cohort. Regimens were classified as three nucleoside reverse transcriptase inhibitors (NRTI), or two NRTIs with a boosted protease inhibitor (bPI), with a non-nucleoside reverse transcriptase inhibitor (NNRTI), or with an INSTI. Patients’ characteristics at the time of initiation were collected. A multinomial logit model was fitted to analyze characteristics related to the choice of regimen in 2015. Results We analyzed data from 15,897 patients. The proportion of patients starting with (i) a bPI decreased from 60% before 2014 to 38.1% in 2015; (ii) an NNRTI decreased from 30% to 17.8% in 2015; (iii) an INSTI gradually increased to 39.4% in 2015. In 2015, patients with an initial viral load ˃5 log copies/mL were less likely to receive NNRTI (OR = 0.08) or INSTI regimens (OR = 0.69) than bPIs. Patients with initial CD4 + T cell count ˂200/mm 3 were less likely to receive an NNRTI (OR = 0.28) or an INSTI regimen (OR = 0.52) than a bPI. Women were less likely to receive an NNRTI (OR = 0.79) or an INSTI regimen (OR = 0.71) than a bPI; although this depended on age. Conclusion The use of bPI as first-line ART declined sharply in France from 2005 to 2015. bPI remained of preferential use in patients with high viral load, low CD4 + T cell count, and in women.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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".