Effects of the Switching From High-Purity Eicosapentaenoic Acid to Combination of Eicosapentaenoic Acid and Docosahexaenoic Acid on Metabolic Parameters: A Retrospective Longitudinal Study
Bibliographic record
Abstract
Background: Effects of the switching from high-purity eicosapentaenoic acid ester (hp-EPA) to n-3 fatty acid formulation containing EPA and docosahexaenoic acid (DHA) on metabolic parameters remain largely unknown. Methods: We retrospectively picked up patients who had been prescribed EPA + DHA for 2 months or longer after the prescription of hp-EPA for 2 months or longer between January 2010 and December 2015. We compared the data at baseline and at 2 months after the switching from hp-EPA to EPA + DHA. Results: Thirty-six patients were eligible for the analyses in our study. Serum triglyceride (TG) showed a non-significant decrease by approximately 9% after the switching. Serum TG showed a non-significant decrease by 24% and 10%, in the switching to the formulation including half daily dose of EPA and DHA and the formulation including the same daily dose of EPA and DHA, respectively. In patients who had shown a decrease in TG after the switching, serum TG at baseline was higher than that in patients who had shown an increase and non-change in TG after the switching. Further, in patients who had shown a decrease in TG after the switching to the formulation including the same daily dose of EPA and DHA, serum TG was significantly higher than that in patients who had shown an increase and non-change in TG. In the analysis of all patients, in patients with baseline TG ? 150 mg/dL, TG tended to decrease. In the analysis of patients who underwent the switching to the formulation including the same daily dose of EPA and DHA, in patients with baseline TG ? 150 mg/dL, TG significantly decreased. Conclusion: We studied effects of the switching from hp-EPA to EPA + DHA on metabolic parameters, and found that the switching is more effective to reduce TG in patients with higher TG levels at baseline. J Endocrinol Metab. 2016;6(3):75-79 doi: http://dx.doi.org/10.14740/jem348w
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".