Prediction of hard cardiovascular events in HIV patients
Bibliographic record
Abstract
OBJECTIVES: To assess the accuracy of risk prediction algorithms used in the general population and an HIV-specific algorithm to predict hard cardiovascular events. METHODS: We compared the pooled equation algorithm (PE) proposed by the American Heart Association with the Framingham risk score (FRS) and the HIV-specific DAD (Data Collection on Adverse Effects of Anti-HIV Drugs) algorithm in a cohort of 2550 HIV+ patients followed for 17 337 patient-years. RESULTS: During follow-up we recorded 67 myocardial infarctions and 2 cardiovascular deaths. PE and FRS identified and missed the same number of events (44 of 69 identified by PE and 49 of 69 by FRS). Similarly, DAD and FRS predicted and missed the same number of events (38 of 64 and 44 of 64 identified, respectively). All algorithms showed moderate sensitivity, specificity and positive predictive values, but high negative predictive values. However, PE and DAD identified more patients with no events than FRS (13.8% and 9.3% net reclassification improvement, respectively). CONCLUSIONS: All algorithms showed a modest predictive ability, although the PE and DAD algorithms identified more patients at low risk.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".