Metabolomic Analysis in Neurocritical Care Patients
Bibliographic record
Abstract
Metabolomics is the analytical study of metabolites in biological matrices using high-throughput profiling. Traditionally, the metabolome has been studied to identify various biomarkers for the diagnosis and pathophysiology of disease. Over the last decade, metabolomic research has grown to include the identification of prognostic markers, the development of novel treatment strategies, and the prediction of disease severity. In this review, we summarized the available evidence on the use of metabolome profiling in neurocritical care populations. Specifically, we focused on aneurysmal subarachnoid hemorrhage, traumatic brain injury, and intracranial hemorrhage to identify the gaps in the current literature and to provide direction for future studies. A primary literature search of the Medline and EMBASE databases was conducted. Upon removing duplicate studies, abstract screening and full-text screening were performed. We screened 648 studies and extracted data from 17 studies. Based on the current evidence, the utility of metabolomic profiling has been limited due to inconsistencies amongst studies and a lack of reproducible data. Studies identified various biomarkers for diagnosis, prognosis, and treatment modification. However, studies evaluated and identified different metabolites, resulting in an inability to compare the study results. Future research towards addressing the gaps in the current literature, including reproducing data on the use of specific metabolite panels, is needed.
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 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 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".