Steps Toward Minimal Reporting Standards for Lipidomics Mass Spectrometry in Biomedical Research Publications
Bibliographic record
Abstract
Lipids in blood and tissues can serve as markers of normal and pathophysiological function in humans and can even reflect functions in specific tissues and organs. Lipidomics describes the analysis of large numbers of lipids using mass spectrometry (MS). The proper implementation of these methods in a manner that ensures data quality requires care and rigorous manual checking. Issues of reproducibility and overall data quality in publications and guidelines for authors submitting research are well-developed for areas that include genetics/genomics, proteomics, and clinical trials. For example, the Human Proteome Organization has developed minimum information publication guidelines for proteomics (https://www.hupo.org/HUPO-Minimum-Information-Publication-Guidelines). However, apart from specialized lipid publications, such as the Journal of Lipid Research, which adopted the Lipid Metabolites and Pathways Strategy Consortium (LIPID MAPS) classification, nomenclature, and structural drawing formats in their guidelines,1,2 there are few reporting guidelines in use for lipidomics data. This issue is particularly relevant to studies that are not focused on underpinning methodological approaches but instead cover broader issues of human health and disease. In many such articles, multiple analytical methods are applied, making it difficult to engage sufficient technical expertise to afford rigorous and comprehensive review. \n \nWe developed a short set of guidelines for lipidomics submissions that we hope will contribute to improving reproducibility and standards in published work (Table). This is a living document, expected to be expanded as the field evolves. It is not intended to serve as a definitive final set of guidelines. To support this sort of activity, the Lipidomics Standard Initiative was recently established to create guidelines for major lipidomic workflows.3
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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 | 0.492 | 0.623 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.025 | 0.017 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.033 | 0.025 |
| Open science | 0.022 | 0.018 |
| Research integrity | 0.018 | 0.025 |
| Insufficient payload (model declined to judge) | 0.014 | 0.027 |
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".