Limitations in the conventional assessment of the incremental value of predictors of cardiovascular risk
Bibliographic record
Abstract
PURPOSE OF REVIEW: Whether a factor significantly increases the area under the curve (AUC) of a receiver operating characteristic analysis has become the standard test of its utility. Thus, in many studies, apolipoprotein B and LDL particle number have not increased the AUC significantly beyond that produced by the conventional markers, and guideline groups have concluded on this basis that they should not be added to routine clinical practice. This article demonstrates this conclusion to be invalid. RECENT FINDINGS: In conventional analyses, no distinctions have been drawn as to whether a novel predictor is causal or whether it is highly correlated with other markers already included in the risk algorithm. However, correlation among the markers will profoundly affect the incremental effect of a factor on the AUC. This distinction is particularly critical for factors that have been shown to play a causal role in the production of clinical event. Accordingly, the AUC approach is valid to determine the total discriminatory ability of a set of variables but is not appropriate to allocate attributable risk among the members of the set. SUMMARY: For correlated predictors that describe different aspects of the same variable such as non-HDL-C and apoB or LDL-C and LDL particle number, discordance analysis offers a simple valid alternative to capture and compare the independent information contained by each predictor.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| 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".