Predictive Accuracy of the Veterans Aging Cohort Study Index for Mortality With HIV Infection
Bibliographic record
Abstract
BACKGROUND: By supplementing an index composed of HIV biomarkers and age (restricted index) with measures of organ injury, the Veterans Aging Cohort Study (VACS) index more completely reflects risk of mortality. We compare the accuracy of the VACS and restricted indices (1) among subjects outside the Veterans Affairs Healthcare System, (2) more than 1-5 years of prior exposure to antiretroviral therapy (ART), and (3) within important patient subgroups. METHODS: We used data from 13 cohorts in the North American AIDS Cohort Collaboration (n = 10, 835) limiting analyses to HIV-infected subjects with at least 12 months exposure to ART. Variables included demographic, laboratory (CD4 count, HIV-1 RNA, hemoglobin, platelets, aspartate and alanine transaminase, creatinine, and hepatitis C status), and survival. We used C-statistics and net reclassification improvement (NRI) to test discrimination varying prior ART exposure from 1 to 5 years. We then combined Veterans Affairs Healthcare System (n = 5066) and North American AIDS Cohort Collaboration data, fit a parametric survival model, and compared predicted to observed mortality by cohort, gender, age, race, and HIV-1 RNA level. RESULTS: Mean follow-up was 3.3 years (655 deaths). Compared with the restricted index, the VACS index showed greater discrimination (C-statistics: 0.77 vs. 0.74; NRI: 12%; P < 0.0001). NRI was highest among those with HIV-1 RNA <500 copies per milliliter (25%) and age ≥50 years (20%). Predictions were similar to observed mortality among all subgroups. CONCLUSIONS: VACS index scores discriminate risk and translate into accurate mortality estimates over 1-5 years of exposure to ART and for diverse patient subgroups from North American.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".