Pantesin, an Active Derivative of Vitamin B5, Used as a Nutritional Supplement, Significantly Lowers Cardiovascular (CV) Disease Markers in Low to Intermediate CV Risk North American Subjects: A Triple‐Blind Placebo/Diet Controlled Study
Bibliographic record
Abstract
Serum cholesterol (C), LDL‐C, non‐HDL‐C, and apolipoprotein B (apo‐B) are well established risk markers for CV disease. The effect (and safety) of Pantesin (Daiichi Fine Chemicals) versus placebo on these risk markers was studied in 120 subjects (n=60/group) who were low to intermediate CV disease risk (NCEP ATP III) at screening. All subjects completed a 4 week diet lead‐in (TLC – Therapeutic Lifestyle Change) prior to randomization (baseline). Results for LDL‐C: LDL‐C (mg/dL) Screening Baseline 4‐weeks 8‐weeks 16‐weeks Placebo 115±26 108±25 112±28 114±29 115±27 Pantesin 112±24 (NS) 112±31 (NS) 104±27 104±28 108±30 = p≤0.005 NS = non‐significant; data are mean±SD Conclusion Pantesin supplementation for 16 weeks (600 mg/d for 8 weeks then 900 mg/d for 8 weeks) significantly lowered LDL‐C as well as apo‐B (p=0.010) and non‐HDL‐C (p<0.001) over and above the effect of TLC diet alone. Pantesin was well tolerated with no differences between groups in adverse events. These results are noteworthy as prior studies have shown each 1 mg/dL reduction in LDL‐C lowers CV risk by 1%.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".