Bibliographic record
Abstract
Traditionally the laboratory diagnosis of inborn errors of metabolism largely relies on targeted hypothesis‐driven measurements of metabolites in body fluids. The hypothesis is usually based on the phenotypic characteristics of the patient, defined through deep phenotyping or phenomics. The biochemical phenotype, or rather the ‘metabolite profile’ reflects both endogenous factors such as genotype(s), gene expression, the different chemical reactions taking place in the body, as well as exogenous factors such as dietary habits, drug metabolism and the microbiome. The last decade has seen the emergence of untargeted, hypothesis‐free measurements. NMR spectroscopy and mass spectrometry in combination with different separation techniques (LC, GC or CE) pave the way to an important next step in our understanding of inborn errors of metabolism. The aim of untargeted metabolomics is to identify and measure as many metabolites as possible, including unknowns, to generate the metabolic fingerprint characteristic for a biological sample and indicative for genetic defects influencing human metabolism. Typically, untargeted metabolomics techniques show more than 10,000 “features” in a single body fluid sample. These are big data! Bioinformatic‐ and chemometric techniques are required to reveal the relevant diagnostic features, which in turn help identify the specific aetiologic condition in the individual patient. This approach is rather different from classical metabolomics studies that usually describe the comparison between a patient group and a control group. This issue of the journal illustrates that “next generation metabolic screening” (NGMS) techniques enable us to use untargeted metabolomics for diagnostics in a clinical setting (Coene et al 2018). Untargeted metabolomics techniques are approaching maturity. It is demonstrated in this special JIMD issue that untargeted metabolomics techniques can identify the vast majority of IEM‐diagnoses as reliably as the conventional techniques. In addition, many examples from NMR spectroscopy and untargeted MS analyses have already emerged that unravel the identity of hitherto unknown inborn errors of metabolism (van Karnebeek et al 2016; Pol et al 2018). These techniques also have the power to discover novel biomarkers even in well‐known and in‐depth studied inborn errors of metabolism (Abela et al 2016; Abela et al 2017).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.041 | 0.019 |
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".