Discordance analysis and the Gordian Knot of LDL and non-HDL cholesterol versus apoB
Bibliographic record
Abstract
PURPOSE OF REVIEW: Conventional methods, comparing the concentration of cholesterol to particle number as indices of cardiovascular risk, have not produced consistent results, in large part, because they treat these variables as independent and unrelated. However, although highly correlated, apolipoprotein B particles may contain a normal mass of cholesterol or may be cholesterol-depleted or cholesterol-enriched. Discordance analysis compares the predictive power of LDL-C and non-HDL-C to apolipoprotein B and LDL particle numbers in patients in whom they differ, that is, in whom they are discordant. The advantage of discordance analysis is that the results are not diluted by concordant data in which risk predictions cannot differ. RECENT FINDINGS: The evidence, to date, consistently demonstrates that apolipoprotein B and LDL particle numbers are more accurate indices of cardiovascular risk than LDL-C or non-HDL-C. SUMMARY: Discordance analysis is a methodological advance that allows the clinical value of closely correlated variables to be determined and demonstrates that cardiovascular risk is more closely related to the number of atherogenic particles than to the total mass of cholesterol within them.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".