The Epidemiology of IRIS in Southern India: An Observational Cohort Study
Bibliographic record
Abstract
Immune reconstitution inflammatory syndrome (IRIS) is an uncommon but dynamic phenomenon seen among patients initiating antiretroviral therapy (ART). We aimed to describe incidence, risk factors, clinical spectrum, and outcomes among ART-naive patients experiencing IRIS in southern India. Among 599 eligible patients monitored prospectively between 2012 and 2014, there were 59.3% males, with mean age 36.6 ± 7.8 years. Immune reconstitution inflammatory syndrome incidence rate was 51.3 per 100 person-years (95% confidence interval: 44.5-59.2). One-third (31.4%) experienced at least 1 IRIS event, at a median of 27 days since ART initiation. Mucocutaneous infections and candidiasis were common IRIS events, followed by tuberculosis. Significant risk factors included age >40 years, body mass index <18.5 kg/m 2 , CD4 count <100 cells/mm 3 , viral load >10 000 copies/mL, hemoglobin <11 g/dL, and erythrocyte sedimentation rate >50 mm/h. Immune reconstitution inflammatory syndrome–related morality was 1.3% (8 of 599); 3 patients died of complicated diarrhea. These findings highlight the current spectrum of IRIS in South India and underscore the importance of heightened vigilance for anemia and treatment of diarrhea and candidiasis during ART initiation.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".