Biomarkers for the acute respiratory distress syndrome: how to make the diagnosis more precise
Bibliographic record
Abstract
The acute respiratory distress syndrome (ARDS) is an acute inflammatory process of the lung caused by a direct or indirect insult to the alveolar-capillary membrane. Currently, ARDS is diagnosed based on a combination of clinical and physiological variables. The lack of a specific biomarker for ARDS is arguably one of the most important obstacles to progress in developing novel treatments for ARDS. In this article, we will review the current understanding of some appealing biomarkers that have been measured in human blood, bronchoalveolar lavage fluid (BALF) or exhaled gas that could be used for identifying patients with ARDS, for enrolling ARDS patients into clinical trials, or for better monitoring of patient's management. After a literature search, we identified several biomarkers that are associated with the highest sensitivity and specificity for the diagnosis or outcome prediction of ARDS: receptor for advanced glycation end-products (RAGE), angiopoietin-2 (Ang-2), surfactant protein D (SP-D), inteleukin-8, Fas and Fas ligand, procollagen peptide (PCP) I and III, octane, acetaldehyde, and 3-methylheptane. In general, these are cell-specific for epithelial or endothelial injury or involved in the inflammatory or infectious response. No biomarker or biomarkers have yet been confirmed for the diagnosis of ARDS or prediction of its prognosis. However, it is anticipated that in the near future, using biomarkers for defining ARDS, or for determining those patients who are more likely to benefit from a given therapy will have a major effect on clinical practice.
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 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 | 0.017 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".