Discrimination and Calibration of the Veterans Aging Cohort Study Index 2.0 for Predicting Mortality Among People With Human Immunodeficiency Virus in North America
Bibliographic record
Abstract
BACKGROUND: The updated Veterans Aging Cohort Study (VACS) Index 2.0 combines general and human immunodeficiency virus (HIV)-specific biomarkers to generate a continuous score that accurately discriminates risk of mortality in diverse cohorts of persons with HIV (PWH), but a score alone is difficult to interpret. Using data from the North American AIDS Cohort Collaboration (NA-ACCORD), we translate VACS Index 2.0 scores into validated probability estimates of mortality. METHODS: Because complete mortality ascertainment is essential for accurate calibration, we restricted analyses to cohorts with mortality from the National Death Index or equivalent sources. VACS Index 2.0 components were ascertained from October 1999 to April 2018. Mortality was observed up to March 2019. Calibration curves compared predicted (estimated by fitting a gamma model to the score) to observed mortality overall and within subgroups: cohort (VACS/NA-ACCORD subset), sex, age <50 or ≥50 years, race/ethnicity, HIV-1 RNA ≤500 or >500 copies/mL, CD4 count <350 or ≥350 cells/µL, and years 1999-2009 or 2010-2018. Because mortality rates have decreased over time, the final model was limited to 2010-2018. RESULTS: Among 37230 PWH in VACS and 8061 PWH in the NA-ACCORD subset, median age was 53 and 44 years; 3% and 19% were women; and 48% and 39% were black. Discrimination in NA-ACCORD (C-statistic = 0.842 [95% confidence interval {CI}, .830-.854]) was better than in VACS (C-statistic = 0.813 [95% CI, .809-.817]). Predicted and observed mortality largely overlapped in VACS and the NA-ACCORD subset, overall and within subgroups. CONCLUSIONS: Based on this validation, VACS Index 2.0 can reliably estimate probability of all-cause mortality, at various follow-up times, among PWH in North America.
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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 | 0.042 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".