Acute Phase Reactants in Infections: Evidence-Based Review and a Guide for Clinicians
Bibliographic record
Abstract
Acute-phase reactants such as erythrocyte sedimentation rate and C-reactive protein have traditionally been used as markers for inflammation and as a measure of "sickness index" in infectious and noninfectious conditions. In the last decade, more data have become available on the wider and more specific role for these markers in the management of complex infections. This includes the potential role in early diagnosis, in differentiating infectious from noninfectious causes, as a prognostic marker, and in antibiotic guidance strategies. A better defined role for biological markers as a supplement to clinical assessment may lead to more judicious antibiotic prescriptions, and it has the potential for a long-term favorable impact on antimicrobial stewardship and antibiotic resistance. Procalcitonin as a biological marker has been of particular interest in this regard. This review examines the current published evidence and summarizes the role of various acute-phase markers in infections. A MEDLINE search of English-language articles on acute-phase reactants and infections published between 1986 and March 2015 was conducted. Additional articles were also identified through a search of references from the retrieved articles, published guidelines, systematic reviews, and meta-analyses.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".