HIV Care and Viral Suppression During the Last Year of Life: A Comparison of HIV-Infected Persons Who Died of HIV-Attributable Causes With Persons Who Died of Other Causes in 2012 in 13 US Jurisdictions
Bibliographic record
Abstract
BACKGROUND: Little information is available about care before death among human immunodeficiency virus (HIV)-infected persons who die of HIV infection, compared with those who die of other causes. OBJECTIVE: The objective of our study was to compare HIV care and outcome before death among persons with HIV who died of HIV-attributable versus other causes. METHODS: We used National HIV Surveillance System data on CD4 T-lymphocyte counts and viral loads within 12 months before death in 2012, as well as on underlying cause of death. Deaths were classified as "HIV-attributable" if the reported underlying cause was HIV infection, an AIDS-defining disease, or immunodeficiency and as attributable to "other causes" if the cause was anything else. Persons were classified as "in continuous care" if they had ≥2 CD4 or viral load test results ≥3 months apart in those 12 months and as having "viral suppression" if their last viral load was <200 copies/mL. RESULTS: Among persons dying of HIV-attributable or other causes, respectively, 65.28% (2104/3223) and 30.88% (1041/3371) met AIDS criteria within 12 months before death, and 33.76% (1088/3223) and 50.96% (1718/3371) had viral suppression. The percentage of persons who received ≥2 tests ≥3 months apart did not differ by cause of death. Prevalence of viral suppression for persons who ever had AIDS was lower among those who died of HIV but did not differ by cause for those who never had AIDS. CONCLUSIONS: The lower prevalence of viral suppression among persons who died of HIV than among those who died of other causes implies a need to improve viral suppression strategies to reduce mortality due to HIV infection.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".