Making respiratory care safe for neonatal and paediatric intensive care unit staff: Mitigation strategies and use of filters
Bibliographic record
Abstract
Background: Many medical devices in pediatric and newborn intensive care units can potentially expose healthcare workers (HCWs) and others to transmission of respiratory and other viruses and bacteria. Such fomites include ventilators, nebulizers, and monitoring equipment. Approach: We report the general, novel approach we have taken to identify and mitigate these risks and to protect HCWs, visitors and patients from exposure while maintaining the optimal performance of such respiratory equipment. Findings: The approach combined a high level of personal protective equipment (PPE), strict hand hygiene, air filtration and air conditioning and other relevant viral risk mitigation guidelines. This report describes the experiences from the SARS-CoV-2 pandemic to provide a reference framework that can be applied generally. The steps we took consisted of auditing our equipment and processes to identify risk through sources of potentially contaminated gas that may contain aerosolized virus, seeking advice and liaising with suppliers/manufacturers, devising mitigation strategies using indirect and direct approaches (largely filtering), performing tests on equipment to verify proper function and the absence of negative impacts and the development and implementation of relevant procedures and practices. We had a multidisciplinary team to guide the process. We monitored daily for hospital-acquired infections among staff caring for SARS-CoV-2 patients. Conclusion: Our approach was successful as we have continued to offer optimal intensive care to our patients, and we did not find any healthcare worker who was infected through the course of caring for patients at the bedside. The lessons learnt will be of benefit to future local outbreaks or pandemics.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".