Specificity of the commonly used enzymatic assay for plasma cholesterol determination: Figure 1
Bibliographic record
Abstract
AIM: To assess the specificity and sensitivity of the commonly used enzymatic colorimetric test for plasma cholesterol determination. METHODS: Interference with an enzymatic method for cholesterol measurement by several non-cholesterol sterols (beta sitosterol, campesterol, stigmasterol, stigmastanol, desmosterol, and lathosterol) was assessed. Some of these compounds are present in plasma at higher than normal concentrations either in rare genetic disorders, such as phytosterolaemia, or after the consumption of phytosterol enriched foods. RESULTS: The non-cholesterol sterols were detected by the assay in a linear manner. There was no competitive interference in the presence of cholesterol. CONCLUSIONS: This crossreactivity may affect the diagnosis and treatment of non-cholesterol dyslipidaemias, including phytosterolaemia and cerebrotendinous xanthomatosis. Similarly, changes in plasma lipid compositions after the consumption of phytosterol enriched foods cannot be specifically determined by this enzymatic assay. Until a more specific enzymatic assay is developed, alternative methods such as gas chromatography should be used to differentiate between cholesterol and non-cholesterol sterols.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.008 |
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".