Procalcitonin to Guide Initiation and Duration of Antibiotic Treatment in Acute Respiratory Infections: An Individual Patient Data Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Procalcitonin algorithms may reduce antibiotic use for acute respiratory tract infections (ARIs). We undertook an individual patient data meta-analysis to assess safety of this approach in different ARI diagnoses and different clinical settings. METHODS: We identified clinical trials in which patients with ARI were assigned to receive antibiotics based on a procalcitonin algorithm or usual care by searching the Cochrane Register, MEDLINE, and EMBASE. Individual patient data from 4221 adults with ARIs in 14 trials were verified and reanalyzed to assess risk of mortality and treatment failure-overall and within different clinical settings and types of ARIs. RESULTS: Overall, there were 118 deaths in 2085 patients (5.7%) assigned to procalcitonin groups compared with 134 deaths in 2126 control patients (6.3%; adjusted odds ratio, 0.94; 95% confidence interval CI, .71-1.23)]. Treatment failure occurred in 398 procalcitonin group patients (19.1%) and in 466 control patients (21.9%; adjusted odds ratio, 0.82; 95% CI, .71-.97). Procalcitonin guidance was not associated with increased mortality or treatment failure in any clinical setting or ARI diagnosis. Total antibiotic exposure per patient was significantly reduced overall (median [interquartile range], from 8 [5-12] to 4 [0-8] days; adjusted difference in days, -3.47 [95% CI, -3.78 to -3.17]) and across all clinical settings and ARI diagnoses. CONCLUSIONS: Use of procalcitonin to guide initiation and duration of antibiotic treatment in patients with ARIs was effective in reducing antibiotic exposure across settings without an increase in the risk of mortality or treatment failure. Further high-quality trials are needed in critical-care patients.
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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 | 0.026 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.063 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".