Systematic Review and Meta-Analysis of Procalcitonin-Guidance Versus Usual Care for Antimicrobial Management in Critically Ill Patients: Focus on Subgroups Based on Antibiotic Initiation, Cessation, or Mixed Strategies*
Bibliographic record
Abstract
OBJECTIVE: Numerous studies have evaluated the use of procalcitonin guidance during different phases of antibiotics management (initiation, cessation, or a combination of both) in patients admitted to ICUs. Several meta-analyses have attempted to generate an overall effect of procalcitonin-guidance on patient outcomes. However, combining studies from different phases of antibiotics management may not be appropriate due to the risk of clinical heterogeneity. The purpose of this systematic review and meta-analysis was to evaluate the effect of procalcitonin-guided strategies in different phases of antibiotics use. DATA SOURCES: We searched MEDLINE and EMBASE from inception until November 1, 2017. STUDY SELECTION: We included randomized controlled trials that evaluated procalcitonin guidance compared with usual care for management of antibiotics in critically ill adult patients. DATA EXTRACTION: We extracted study details, patient characteristics, procalcitonin algorithm, and outcomes. DATA SYNTHESIS: We included 15 studies, from 1,624 abstracts identified based on our search strategy (three initiation, nine cessation, and three mixed). The pooled risk ratio for short-term mortality for the initiation, cessation, and mixed procalcitonin strategies were 1.00 (95% CI, 0.86-1.15,;p = 0.91), 0.87 (95% CI, 0.77-0.98; p = 0.02), and 1.01 (95% CI, 0.80-1.29; p = 0.93), respectively. Procalcitonin for cessation and mixed strategies was associated with decrease antibiotics duration (-1.26 d [p < 0.001] and -3.10 d [p =0.04], respectively). No differences were observed in other outcome measures. CONCLUSION: When evaluating all studies of procalcitonin-guided antibiotics management in critically ill patients, no difference in short-term mortality was observed. However, when only examining procalcitonin-guided cessation of antibiotics, lower mortality was detected. Future studies should focus specifically on procalcitonin for the cessation of antibiotics in critically ill patients.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".