Incidence of AIDS-Defining Opportunistic Infections in a Multicohort Analysis of HIV-infected Persons in the United States and Canada, 2000–2010
Bibliographic record
Abstract
BACKGROUND: There are few recent data on the rates of AIDS-defining opportunistic infections (OIs) among human immunodeficiency virus (HIV)-infected patients in care in the United States and Canada. METHODS: We studied HIV-infected participants in 16 cohorts in the North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD) during 2000-2010. After excluding 16 737 (21%) with any AIDS-defining clinical events documented before NA-ACCORD enrollment, we analyzed incident OIs among the remaining 63 541 persons, most of whom received antiretroviral therapy during the observation. We calculated incidence rates per 100 person-years of observation (hereafter, "person-years") with 95% confidence intervals (CIs) for the first occurrence of any OI and select individual OIs during 2000-2003, 2004-2007, and 2008-2010. RESULTS: A total of 63 541 persons contributed 261 573 person-years, of whom 5836 (9%) developed at least 1 OI. The incidence rate of any first OI decreased over the 3 observation periods, with 3.0 cases, 2.4 cases, and 1.5 cases per 100 person-years of observation during 2000-2003, 2004-2007, and 2008-2010, respectively (Ptrend<.001); the rates of most individual OIs decreased as well. During 2008-2010, the leading OIs included Pneumocystis jiroveci pneumonia, esophageal candidiasis, and disseminated Mycobacterium avium complex or Mycobacterium kansasii infection. CONCLUSIONS: For HIV-infected persons in care during 2000-2010, rates of first OI were relatively low and generally declined over this time.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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".