ARDS metabolic fingerprints: characterization, benchmarking, and potential mechanistic interpretation
Bibliographic record
Abstract
In this study, we aimed to identify acute respiratory distress syndrome (ARDS) metabolic fingerprints in selected patient cohorts and compare the metabolic profiles of direct versus indirect ARDS and hypoinflammatory versus hyperinflammatory ARDS. We hypothesized that the biological and inflammatory processes in ARDS would manifest as unique metabolomic fingerprints that set ARDS apart from other intensive care unit (ICU) conditions and could help examine ARDS subphenotypes and clinical subgroups. Patients with ARDS ( n = 108) and ICU ventilated controls ( n = 27) were included. Samples were randomly divided into 2/3 training and 1/3 test sets. Samples were analyzed using 1 H nuclear magnetic resonance spectroscopy and gas chromatography-mass spectrometry. Twelve proteins/cytokines were also measured. Orthogonal partial least squares discriminant analysis (OPLS-DA) was used to select the most differentiating ARDS metabolites and protein/cytokines. Predictive performance of OPLS-DA models was measured in the test set. Temporal changes of metabolites were examined as patients progressed through ARDS until clinical recovery. Metabolic profiles of direct versus indirect ARDS subgroups and hypoinflammatory versus hyperinflammatory ARDS subgroups were compared. Serum metabolomics and proteins/cytokines had similar area under receiver operator curves when distinguishing ARDS from ICU controls. Pathway analysis of ARDS differentiating metabolites identified a dominant involvement of serine-glycine metabolism. In longitudinal tracking, the identified pathway metabolites generally exhibited correction by 7−14 days, coinciding with clinical improvement. ARDS subphenotypes and clinical subgroups were metabolically distinct. However, our identified metabolic fingerprints are not ARDS diagnostic biomarkers, and further research is required to ascertain generalizability. In conclusion, patients with ARDS are metabolically different from ICU controls. ARDS subphenotypes and clinical subgroups are metabolically distinct.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".