Characterization and outcomes of difficult-to-treat patients starting modern first-line ART regimens: Data from the ICONA cohort
Bibliographic record
Abstract
OBJECTIVES: Treatment failures to modern antiretroviral therapy (ART) raise concerns, as they could reduce future options. Evaluations of occurrence of multiple failures to modern ART are missing and their significance in the long run is unclear. METHODS: People with HIV (PWH) in the ICONA cohort who started a modern first-line ART were defined as 'difficult to treat' (DTT) if they experienced ≥1 among: i) ≥2 VF (2 viral loads, VL>200 copies/mL or 1 VL>1000 copies/mL) with or without ART change; ii) ≥2 treatment discontinuations (TD) due to toxicity/intolerance/failure; iii) ≥1 VF followed by ART change plus ≥1 TD due to toxicity/intolerance/failure. A subgroup of the DTT participants were matched to PWH that, after the same time, were non-DTT. Treatment response, analysing VF, TD, treatment failure, AIDS/death, and SNAE (Serious non-AIDS event)/death, were compared. Survival analysis by KM curves and Cox regression models were employed. RESULTS: Among 8061 PWH, 320 (4%) became DTT. Estimates of becoming DTT was 6.5% (95% CI: 5.8-7.4%) by 6 years. DTT PWH were significantly older, with a higher prevalence of AIDS and lower CD4+ at nadir than the non-DTT. In the prospective analysis, DTT demonstrated a higher unadjusted risk for all the outcomes. Once controlled for confounders, significant associations were confirmed for VF (aHR 2.23, 1.33-3.73), treatment failure (aHR 1.70, 1.03-2.78), and SNAE/death (aHR 2.79, 1.18-6.61). CONCLUSION: A total of 6.5% of PWH satisfied our definition of DTT by 6 years from ART starting. This appears to be a more fragile group who may have higher risk of 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".