Plasma metabolomics for the diagnosis and prognosis of H1N1 influenza pneumonia
Bibliographic record
Abstract
BACKGROUND: Metabolomics is a tool that has been used for the diagnosis and prognosis of specific diseases. The purpose of this study was to examine if metabolomics could be used as a potential diagnostic and prognostic tool for H1N1 pneumonia. Our hypothesis was that metabolomics can potentially be used early for the diagnosis and prognosis of H1N1 influenza pneumonia. METHODS: H nuclear magnetic resonance spectroscopy and gas chromatography-mass spectrometry were used to profile the metabolome in 42 patients with H1N1 pneumonia, 31 ventilated control subjects in the intensive care unit (ICU), and 30 culture-positive plasma samples from patients with bacterial community-acquired pneumonia drawn within the first 24 h of hospital admission for diagnosis and prognosis of disease. RESULTS: We found that plasma-based metabolomics from samples taken within 24 h of hospital admission can be used to discriminate H1N1 pneumonia from bacterial pneumonia and nonsurvivors from survivors of H1N1 pneumonia. Moreover, metabolomics is a highly sensitive and specific tool for the 90-day prognosis of mortality in H1N1 pneumonia. CONCLUSIONS: This study demonstrates that H1N1 pneumonia can create a quite different plasma metabolic profile from bacterial culture-positive pneumonia and ventilated control subjects in the ICU on the basis of plasma samples taken within 24 h of hospital/ICU admission, early in the course of disease.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".