P0635 / #1815: A PROSPECTIVE COHORT STUDY ON THE EVOLUTION OF INFECTION MARKERS IN CHILDREN WITH SEVERE BACTERIAL INFECTIONS TREATED WITH ANTIBIOTICS
Bibliographic record
Abstract
Aims & Objectives: Inflammatory biomarkers have been used to guide antibiotic duration in adults and neonates, but their utility in children admitted to pediatric intensive care units (PICUs) is unknown. Our objective was thus to characterize the temporal behaviour of biomarkers (C-reactive protein [CRP], procalcitonin, and white blood cell [WBC] count) in children with severe bacterial infections. Methods: Ongoing prospective cohort study in 7 Canadian PICUs including patients 1 month to 18 years old admitted with suspected or proven severe bacterial infection (sepsis, pneumonia, and central nervous system and intrabdominal infections) and who were prescribed ≥1 antimicrobial. We measure biomarker levels from days 1 to 7 and day 10 of antibiotic treatment. We used Pearson’s correlation coefficient and multivariable linear regressions. Results: We have enrolled 208 patients. Median age was 56.0 months (interquartile range [IQR] 14.8 – 103.0). Median antibiotic duration was 9.5 days (IQR 6.0 – 14.0). Absolute levels of CRP and procalcitonin were highest on day 1 and descended over the course of treatment. By day 5, 82% and 43% of patients had ≥80% drop in procalcitonin and CRP levels, respectively. Patients with ≥50% drop in CRP from days 1 to 3 had a shorter hospital stay (-6.4 days, 95% confidence interval [CI] -12.2, -0.8). There was a strong correlation between mean daily values of multiple organ dysfunction syndrome scores with mean daily CRP (0.93, 95%CI 0.66, 0.99) and procalcitonin values (0.98, 95%CI 0.88, 0.99).Conclusions: Preliminary data suggest that CRP and procalcitonin may be good candidate biomarkers to personalize antibiotic treatment duration in children.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".