Reclassification of risk of death with the knowledge of D-dimer in a cohort of treated HIV-infected individuals
Bibliographic record
Abstract
OBJECTIVE: To evaluate the change in categories of risk of death by adding D-dimer to conventional mortality risk factors. DESIGN: Cohort study. METHODS: Data on HIV-infected participants receiving standard combination antiretroviral therapy in two clinical trials (Evaluation of Subcutaneous Proleukin in a Randomized International Trial and Strategic Management of antiretroviral therapy), who had baseline D-dimer measured, were randomly split into two equal training and a validation datasets. A multivariable survival model was built using the training dataset and included only conventional mortality risk factors measured at baseline. D-dimer was added to create the comparison model. The level of reclassification of mortality risk, for those with at least 5-years of follow-up, was then assessed by tabulating mortality risk defined as low (≤2% predicted rate), moderate (2-5%) or high (>5%). Reclassification analyses were then repeated on the validation dataset. RESULTS: The analysis population at baseline had a mean age of 43 years, median CD4(+) cell count of 535 cells/μl (IQR: 420-712), and 83% had HIV RNA of at least 500 copies/ml. In the training dataset (n=1946, 8939 person-years), there were 83 deaths at a rate of 0.93 per 100 person-years. Addition of D-dimer to the reference model resulted in 6% or fewer (P>0.05) being correctly reassigned, either up or down, to a new risk category, in both, training and validation datasets. The integrated discrimination improvement in training and validation datasets was 0.60% (P=0.084) and 0.45% (P=0.168), respectively. CONCLUSION: In this relatively well population, at the given risk cutoffs, D-dimer appeared to only modestly improve the discernment of risk. Risk reclassification provides a method for assessing the clinical utility of biomarkers in HIV cohort studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".