Response to commentary on a trial comparing krill oil versus fish oil
Bibliographic record
Abstract
Nichols et al. (Lipids Health Dis13:2, 2014) raised concern about the higher n-6 concentration in fish oil used in our recent study which is different from typical commercial fish oils (Ramprasath et al. Lipids Health Dis12:178, 2013). The aim of our study was to compare the effect of consumption of similar amount of n-3 PUFA from krill and fish oil with placebo on plasma and RBC fatty acids. As the concentration of n-3 PUFA in the fish oil utilised was higher than that in krill oil, we deemed it important to keep consistent the concentration of n-3 PUFA and volumes to be administered to participants between krill versus fish oils. As such, the fish oil used in the study was diluted with corn oil. Although the n-6 PUFA concentration in fish oil was higher compared to traditionally used fish oil, consumption of the fish oil used in our study actually reduced the total n-6 PUFA in plasma and RBC to a similar extent as did krill oil. Overall, our conclusion was that the increases in plasma and RBC concentrations of EPA and DHA along with improvement in the omega-3 index observed with consumption of krill oil compared with fish oil are due to differences in absorption and bioavailability based on the structural difference of the two oils rather than their n-6 PUFA content.
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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 | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.060 | 0.031 |
| Insufficient payload (model declined to judge) | 0.011 | 0.009 |
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".