Can biomarkers improve the rational use of antibiotics?
Bibliographic record
Abstract
PURPOSE OF REVIEW: We aim to review recent literature about the use of biomarkers to guide the initiation and duration of antibiotic treatments for suspected bacterial infections. RECENT FINDINGS: Several good quality meta-analyses show that procalcitonin can be safely used to guide antibiotic-related decisions, especially for respiratory infections, thereby decreasing unnecessary antibiotic exposure. Furthermore, the use of CRP-based algorithms to guide antibiotic initiation in primary care patients with acute respiratory infections is associated with a reduction in antibiotic use without an increase in adverse events. Regarding neutrophil CD64 and serum amyloid A, more good-quality evidence is needed to assess their utility as biomarkers to tailor antibiotic use. Finally, transcriptomics, metabolomics and proteomics are promising tools for the development of tests to differentiate specific host responses to viral, bacterial and noninfectious stimuli, but such tests still need further validation. SUMMARY: Evidence shows that the use of biomarkers, procalcitonin and CRP, can safely reduce unnecessary antibiotic prescriptions in certain infectious syndromes. The clinical utility of host-based strategies such as transcriptomics, metabolomics and proteomics for the diagnosis of infectious diseases has yet to be evaluated, as well as considerations such as costs, technical complexity and result turnaround time.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".