Lipoprint Adequately Estimates LDL Size Distribution, but not Absolute Size, Versus Polyacrylamide Gradient Gel Electrophoresis
Bibliographic record
Abstract
Recently, a new cost-effective and less labor-intensive technique termed the "lipoprint LDL system" was developed to measure LDL particle size. However, the agreement between lipoprint and previously validated techniques, such as polyacrylamide gradient gel electrophoresis (PGGE), has never been tested. Therefore, we measured LDL size by lipoprint and PGGE in 16 obese subjects at 4 different time points. Lipoprint significantly overestimated (P = 0.003) integrated LDL particle size by 1.1 ± 3.0 Å when compared to PGGE. As for distribution, there was good agreement between methods for the estimation of large, medium, and small particles (mean difference between the methods was <3% for each parameter). Correlational analysis also revealed good relationships between methods for the proportion of large (r = 0.81, P < 0.0001), medium (r = 0.67, P < 0.0001), and small (r = 0.73, P < 0.0001) particles. In sum, although there is good agreement between lipoprint and PGGE for the determination of LDL size distribution, absolute LDL size values may differ between the two methods.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".