Diagnostic Applications of Nuclear Magnetic Resonance–Based Urinary Metabolomics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Metabolomics is a rapidly growing field with potential applications in various disciplines. In particular, metabolomics has received special attention in the discovery of biomarkers and diagnostics. This is largely due to the fact that metabolomics provides critical information related to the downstream products of many cellular and metabolic processes which could provide a snapshot of the health/disease status of a particular tissue or organ. Many of these cellular products eventually find their way to urine; hence, analysis of urine via metabolomics has the potential to yield useful diagnostic and prognostic information. Although there are a number of analytical platforms that can be used for this purpose, this review article will focus on nuclear magnetic resonance-based metabolomics. Furthermore, although there have been many studies addressing different diseases and metabolic disorders, the focus of this review article will be in the following specific applications: urinary tract infection, kidney transplant rejection, diabetes, some types of cancer, and inborn errors of metabolism. A number of methodological considerations that need to be taken into account for the development of a clinically useful optimal test are discussed briefly.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it