SHEA Neonatal Intensive Care Unit (NICU) White Paper Series: Practical approaches for the prevention of central-line–associated bloodstream infections
Bibliographic record
Abstract
Abstract This document is part of the “SHEA Neonatal Intensive Care Unit (NICU) White Paper Series.” It is intended to provide practical, expert opinion, and/or evidence-based answers to frequently asked questions about CLABSI detection and prevention in the NICU. This document serves as a companion to the CDC Healthcare Infection Control Practices Advisory Committee (HICPAC) Guideline for Prevention of Infections in Neonatal Intensive Care Unit Patients. Central line-associated bloodstream infections (CLABSIs) are among the most frequent invasive infections among infants in the NICU and contribute to substantial morbidity and mortality. Infants who survive CLABSIs have prolonged hospitalization resulting in increased healthcare costs and suffer greater comorbidities including worse neurodevelopmental and growth outcomes. A bundled approach to central line care practices in the NICU has reduced CLABSI rates, but challenges remain. This document was authored by pediatric infectious diseases specialists, neonatologists, advanced practice nurse practitioners, infection preventionists, members of the HICPAC guideline-writing panel, and members of the SHEA Pediatric Leadership Council. For the selected topic areas, the authors provide practical approaches in question-and-answer format, with answers based on consensus expert opinion within the context of the literature search conducted for the companion HICPAC document and supplemented by other published information retrieved by the authors. Two documents in the series precede this one: “Practical approaches toClostridioides difficileprevention” published in August 2018 and “Practical approaches toStaphylococcus aureusprevention,” published in September 2020.
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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 | 0.014 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.045 | 0.027 |
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".