HIV Disease Progression to CD4 Count <200 Cells/μL and Death in Saskatoon, Saskatchewan
Bibliographic record
Abstract
OBJECTIVE: To characterize and identify determinants of HIV disease progression among a predominantly injection drug use (IDU) HIV population in the highly active antiretroviral therapy era. METHODS: The present retrospective study was based on 343 HIV patients diagnosed from 2005 to 2010 from two clinics in Saskatoon, Saskatchewan. Disease progression was defined as the time from diagnosis to immunological AIDS (CD4 count <200 cells/μL) and death. Uni- and multivariable Cox proportional hazards models were used. RESULTS: Of the 343 patients, 79% had a history of IDU, 77% were hepatitis C virus (HCV) coinfected and 67% were of Aboriginal descent. The one-year and three-year immunological AIDS-free probabilities were 78% and 53%, respectively. The one-year and three-year survival probabilities were 97% and 88%, respectively. Multicollinearity among IDU, HCV and ethnicity was observed and, thus, separate models were built. HCV coinfection (HR 2.9 [95% CI 1.2 to 6.9]) was a significant predictor of progression to immunological AIDS when controlling for baseline CD4 counts, treatment, age at diagnosis and year of diagnosis. For survival, only treatment use was a significant predictor (HR 0.34 [95% CI 0.1 to 0.8]). HCV coinfection was marginally significant (P=0.067). CONCLUSION: Baseline CD4 count, HCV coinfection, year of diagnosis and treatment use were significant predictors of disease progression. This highlights the importance of early treatment and the need for targeted interventions for these particularly vulnerable populations to slow disease progression.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".