Procalcitonin: Is This the Promised Biomarker for Critically Ill Patients?
Bibliographic record
Abstract
Objective Procalcitonin (PCT) has been increasingly used in the critical care setting to determine the presence of bacterial infection and also to guide antibiotic therapy. We reviewed PCT's physiologic role, as well as its clinical utility for the management of pediatric critically ill patients. Findings PCT is a precursor of the hormone calcitonin. Its production is induced by inflammatory conditions, especially bacterial infections. Literature shows that PCT is a moderately reliable diagnostic test for severe bacterial infection in children. Synthesis of available adult studies suggests that the use of PCT-based algorithms to support medical decision making reduces antibiotic exposure without compromising safety in critically ill patients. However, no study has addressed the usefulness and safety of PCT to guide antibiotic therapy in severely ill children. In pediatric patients with acute lower respiratory tract infections, the use of PCT-based algorithms also led to a safe decrease in antibiotic treatment duration. Conclusion PCT has demonstrated clinical utility in the pediatric critical care setting when used for the diagnosis of bacterial infections and to guide antibiotic use in children with acute lower respiratory tract infections. However, more research is needed in critically ill children to determine the utility of PCT-driven antibiotic therapy in this population.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".