The percentage of DHA in erythrocytes can detect non-adherence to advice to increase EPA and DHA intakes
Bibliographic record
Abstract
Characterisation of long-term adherence to EPA and DHA intakes through biomarkers and dietary assessments has implications for interpreting the findings of long-term intervention studies. Adherence to dietary advice targeting an EPA+DHA intake of 1 g/d was examined over 1 year. Men and women (n 45) received dietary advice to increase EPA and DHA intakes from seafood, nutraceutical (fish oil) or functional food sources, while a fourth group received combined advice. Blood biomarkers and dietary intakes of EPA and DHA were evaluated at baseline and post-intervention at weeks 4, 8, 12, 24 and 52. Assessment by 3 d diet records indicated that EPA+DHA intakes increased relative to baseline in weeks 4-52 following the seafood, nutraceutical and combined advice (advice group × time effect, P= 0·03). The percentage of DHA in plasma and whole blood and the percentage of EPA in erythrocytes, plasma and whole blood were higher in weeks 4-52 when compared with the corresponding baseline measurement. In contrast, the percentage of DHA in erythrocytes increased to a maximum at week 12 and returned to baseline levels in weeks 24 and 52 (time effect, P< 0·01). Measurement of the percentage of DHA in erythrocytes indicates that adherence was sustained during the first 12 weeks following the dietary advice, while other blood measurements of the percentage of EPA and DHA and dietary assessment suggest short-term increases in EPA+DHA intakes immediately before weeks 24 and 52. The percentage of DHA in erythrocytes characterises adherence to EPA and DHA intakes in long-term interventions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".