Best practices for the design, laboratory analysis, and reporting of trials involving fatty acids
Bibliographic record
Abstract
Fatty acids are among the most studied nutrients in human metabolism and health. Endogenous fatty acid status influences health and disease via multiple mechanisms at all stages of the life cycle. Despite widespread interest, attempts to summarize the results of multiple studies addressing similar fatty acid-related outcomes via meta-analyses and systematic reviews have been disappointing, largely because of heterogeneity in study design, sampling, and laboratory and data analyses. Our purpose is to recommend best practices for fatty acid clinical nutrition and medical studies. Key issues in study design include judicious choice of sampled endogenous pools for fatty acid analysis, considering relevant physiologic state, duration of intervention and/or observation, consideration of specific fatty acid dynamics to link intake and endogenous concentrations, and interpretation of results with respect to known fatty acid ranges. Key laboratory considerations include proper sample storage, use of sample preparation methods known to be fit-for-purpose via published validation studies, detailed reporting or methods to establish proper fatty acid identification, and quantitative analysis, including calibration of differential response, quality control procedures, and reporting of data on a minimal set of fatty acids to enable comprehensive interpretation. We present a checklist of recommendations for fatty acid best practices to facilitate design, review, and evaluation of studies with the intention of improving study reproducibility.
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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 | 0.724 | 0.852 |
| Meta-epidemiology (narrow) | 0.007 | 0.010 |
| Meta-epidemiology (broad) | 0.012 | 0.013 |
| Bibliometrics | 0.027 | 0.020 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.026 | 0.012 |
| Open science | 0.017 | 0.010 |
| Research integrity | 0.025 | 0.037 |
| Insufficient payload (model declined to judge) | 0.008 | 0.011 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".