SHEA NICU white paper series: Practical approaches for the prevention of viral respiratory infections
Bibliographic record
Abstract
This white paper provides clinicians and hospital leaders with practical guidance on the prevention and control of viral respiratory infections in the neonatal intensive care unit (NICU). This document serves as a companion to Centers for Disease Control and Prevention Healthcare Infection Control Practices Advisory Committee (HICPAC)'s "Prophylaxis and Screening for Prevention of Viral Respiratory Infections in Neonatal Intensive Care Unit Patients: A Systematic Review." It provides practical, expert opinion and/or evidence-based answers to frequently asked questions about viral respiratory detection and prevention in the NICU. It was developed by a writing panel of pediatric and pathogen-specific experts who collaborated with members of the HICPAC systematic review writing panel and the SHEA Pediatric Leadership Council to identify questions that should be addressed. The document has been endorsed by SHEA, the American Hospital Association (AHA), The Joint Commission, the Pediatric Infectious Diseases Society (PIDS), the Association for Professionals in Infection Control and Epidemiology (APIC), the Infectious Diseases Society of America (IDSA), and the National Association of Neonatal Nurses (NANN).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.092 | 0.269 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.035 | 0.020 |
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".