Author's reply: Low-dose eicosapentaenoic acid and/or docosahexaenoic acid and triglyceride lowering
Bibliographic record
Abstract
We are thankful for the insight provided by Maki and Rains regarding our recent work titled “Long-chain omega-3 fatty acids eicosapentaenoic acid and docosahexaenoic acid dose-dependently reduce fasting serum triglycerides.”1 Maki and Rains raised two important issues relevant to our dose-response assessment. First, they suggested that a clear tracking of excluded studies and reasons for their exclusion should have been included in our manuscript to enable the reader to assess the potential for study-selection bias. Second, they raised questions regarding the omission of two of the largest fish oil intervention studies, the GISSI Prevention2 and GISSI Heart Failure3 trials. Each of these issues is discussed sequentially. The literature search described in our publication resulted in the identification of over 10,000 titles, despite attempts to limit the search to studies conducted in humans. This is reflective of the substantial amount of information that has been published on the topic of long-chain omega-3 fatty acids. The literature filtration process consisted of two steps: 1) abstracts of titles determined to be potentially relevant were reviewed; and 2) full-length articles of abstracts determined to be potentially relevant were reviewed. At each of these steps, the study inclusion and exclusion criteria listed in the first table of our publication1 were applied. The studies that were excluded, and the reasons for their exclusion, were tracked only from the point of full-length article review, given the substantial number of titles and abstracts reviewed. To be included in our dose-response assessment, one of …
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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 | 0.010 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.032 | 0.033 |
| Insufficient payload (model declined to judge) | 0.009 | 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".