Collagen-related biomarkers in severe sepsis: a big stretch?
Bibliographic record
Abstract
Biomedical scientists are aggressively investigating biomarkers of disease and injury. The rationale for identifying biomarkers during pathological states, such as severe sepsis, is to improve clinical prognostication and stratify therapeutic interventions for optimal recovery. An added benefit of biomarker studies is knowledge genesis on pathophysiological mechanisms, critical information that provides a basis for hypothesis-driven research. Unfortunately, biomarkers rarely alter our clinical approach in severe sepsis as they are often non-specific, lack adequate sensitivity and/or are difficult to measure and interpret accurately. Given the complexity and heterogeneity of severe sepsis, and the unique genetically derived susceptibilities of individuals, it is highly unlikely that one or even a handful of biomarkers will provide adequate biomedical information for clinical guidance. Thus, biomarkers will ultimately alter clinical decision making only once a panel of promising biomarkers is identified, maximizing sensitivity and specificity, and then adequately scrutinized with quantitative scoring methods over large populations of patients.
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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.027 | 0.022 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".