A Metabolomics approach for the diagnosis Of SecondAry InfeCtions in COVID-19 (MOSAIC): a study protocol
Bibliographic record
Abstract
BACKGROUND: Critically ill patients with COVID-19 are at an increased risk of developing secondary bacterial infections. These are both difficult to diagnose and are associated with an increased mortality. Metabolomics may aid clinicians in diagnosing secondary bacterial infections in COVID-19 through identification and quantification of disease specific biomarkers, with the aim of identifying underlying causative microorganisms and directing antimicrobial therapy. METHODS: This is a multi-centre prospective diagnostic observational study. Patients with COVID-19 will be recruited from critical care units in three Scottish hospitals. Three serial blood samples will be taken from patients, and an additional sample taken if a patient shows clinical or microbiological evidence of secondary infection. Samples will be analysed using LC-MS and subjected to bioinformatic processing and statistical analysis to explore the metabolite changes associated with bacterial infections in COVID-19 patients. Comparisons of the data sets will be made with standard microbiological and biochemical methods of diagnosing infection. DISCUSSION: Metabolomics analyses may provide additional strategies for identifying secondary infections, which might permit faster initiation of specific tailored antimicrobial therapy to critically ill patients with COVID-19.
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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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.008 |
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".