The Clinical Significance of CD4 Counts in Asian and Caucasian HIV-Infected Populations: Results from TAHOD and AHOD
Bibliographic record
Abstract
The significance of interethnic variation in CD4 counts between Asian and Caucasian populations is not known. Patients on combination antiretroviral therapy from Treat Asia and Australian HIV Observational Databases (TAHOD, predominantly Asian, n = 3356; and AHOD, predominantly Caucasian, n = 2312, respectively) were followed for 23 144 person-years for AIDS/death and all-cause mortality endpoints. We calculated incidence-rates and used adjusted Cox regression to test for the interaction between cohort (TAHOD/AHOD) and time-updated CD4 count category (lagged by 3 months) for each of the endpoints. There were 382 AIDS/death events in TAHOD (rate: 4.06, 95%CI: 3.68-4.50) and 305 in AHOD (rate: 2.39, 95%CI: 2.13-2.67), per 100 person-years. At any given CD4 count category, the incidence-rates of endpoints were found to be similar between TAHOD and AHOD (in the adjusted models, P > .05 for the interaction term between cohort type and latest CD4 counts). At any given CD4 count, risk of AIDS or death was not found to vary by ethnicity, suggesting that the CD4 count thresholds for predicting outcomes defined in Caucasian populations may be equally valid in Asian populations.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".