A lipidomic dataset for epidemiological studies of acute myocardial infarction
Bibliographic record
Abstract
Understanding the cause of coronary heart diseases relies on the analysis of data from a range of techniques on an epidemiological scale. Lipidomics, the identification and quantification of lipid species in a system, is an omic approach increasingly used in epidemiology. The altered concentration of lipids in plasma is one of the recognised risk factors for these diseases. An important first step in the analysis is to profile lipids in healthy volunteers at an epidemiological level to understand how the geneome influences risk factors; for this reason we made use of the control samples within a bigger case-control sample collection in Pakistan from patients with first acute myocardial infarctions. After extraction, the samples were infused into a Thermo Exactive Orbitrap, without any up-front chromatographic separation. The use of direct infusion allowed fast experiment, facilitating the analysis of large sets of samples. The raw data were processed and analysed using scripts within R, to extract all the meaningful information. The data set originated from this study is a valuable resource to both increase our knowledge in lipid metabolism associated with myocardial infarction, and test new methods and strategy in analysing big lipidomic data sets.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".