Non-HDL cholesterol and apoB in dyslipidaemia
Bibliographic record
Abstract
On the basis of a high correlation, non-HDL-C (non-high-density lipoprotein cholesterol) and apoB (apolipoprotein B) have been suggested to be of equivalent value for clinical practice; however, the strength of this relationship has not been examined in detail in patients with dyslipidaemia. The present study examines the variance of non-HDL-C compared with apoB in 1771 consecutive patients evaluated in a lipid clinic. These patients were divided into normolipidaemic subjects (n=407), type I hyperlipoproteinaemia (n=16), type IIa (n=736) and IIb (n=231) hyperlipoproteinaemia, type III hyperlipoproteinaemia (n=38), type IV hyperlipoproteinaemia (n=509) and type V hyperlipoproteinaemia (n=101). The relationship between non-HDL-C and apoB was examined both in terms of correlation and concordance. Correlation was high, but concordance was only moderate in the normolipidaemic subjects and in those with type IIa and type IIb hyperlipoproteinaemia. Correlation and concordance were both low in the subgroups with type III and type V hyperlipoproteinaemia. In those with type IV hyperlipoproteinaemia, correlation was moderately high (r=0.74), but concordance was only fair. In conclusion, our results indicate that there is substantial variance of apoB for given values of non-HDL-C in many dyslipidaemic subjects. It follows that correlation is not adequate as a sole judge of equivalence of laboratory parameters.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".