The Role of Procalcitonin in Diagnosis of Sepsis and Antibiotic Stewardship: Opportunities and Challenges
Bibliographic record
Abstract
Sepsis was recently redefined as “a life-threatening organ dysfunction caused by dysregulated host-response to systemic infection” based on the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). The incidence of sepsis is increasing despite global initiatives, with a mortality ranging from 30% to 50%. A timely diagnosis of sepsis is pivotal for prompt recognition and appropriate intervention. Each hour of delay in administration of antibiotics results in an increase of 7.6% mortality for septic shock, yet overdiagnosis and inappropriate use of broad-spectrum antibiotics contribute to the emergence of antibiotic resistance. Diagnosis of sepsis is a clinical challenge. Early signs of systemic inflammation such as fever, tachycardia, and leukocytosis are not specific to sepsis. Traditionally, anaerobic and aerobic blood cultures were used to detect and identify the presence of bacterial infection; however, approximately 40% of patients with sepsis are culture-negative. Other biomarkers such as C-reactive protein (CRP;11 inflammatory) and lactate (organ dysfunction) are not early indicators and lack specificity. There is increasing evidence that support the use of procalcitonin (PCT) for diagnosis of bacterial sepsis and act as a guide to discontinue antibiotic therapy. Yet, there are concerns about the efficacy, safety, and availability of PCT. We have asked 5 experts with different roles in this field to share their thoughts on the challenges of PCT-guided diagnosis and antibiotic therapy. 1. What are the challenges in sepsis diagnosis? Joshua Hayden: In clinical journals, the question is “What are the challenges in defining sepsis?” Systemic inflammatory response syndrome (SIRS) criteria have been removed from the new Sepsis-3 definition after 25 years. A clinical syndrome that is this hard to define, not surprisingly, is difficult to diagnose. It is worth noting that the only laboratory values that currently count toward the diagnosis of sepsis are lactate and those included in …
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".