A Quality Improvement Initiative to Reduce Nasal Injuries Secondary to Nasal Mask Application for Non-Invasive Ventilation
Bibliographic record
Abstract
Introduction: Nasal injury is an important concern with the use of non-invasive ventilation delivered by masks or prongs. Despite regular skin integrity assessment and close monitoring, we found a high incidence rate of nasal skin injuries in nearly 70% of neonates receiving non-invasive ventilation admitted into our neonatal intensive care unit (NICU). Methods: A quality improvement (QI) initiative to reduce the incidence of nasal injuries secondary to nasal mask application in inborn neonates admitted to our NICU by 50% from baseline over 8 weeks. We performed fishbone analysis and multiple Plan-Do-Study-Act (PDSA) cycles targeted to reduce nasal injury following nasal mask application for non-invasive ventilation. A QI team comprising resident doctors, nurses and staff neonatologists was formed to drive change. Key interventions were strengthening the protocol-based application of nasal masks and consistent monitoring and documenting injuries. Data were collected weekly and plotted on run charts. Results: The successive implementation of interventions led to the reduction of injury by 46% (71%–25%) at the end of 5 weeks of intervention. The interventions have been incorporated into our unit policy and have sustained at 45% for 3 months following the QI initiative. Conclusion: QI methods involving training and educating healthcare workers about the step-by-step application of nasal masks and frequent assessment for nasal injury helped reduce the nasal injury rate in our unit. Incorporation into the NICU policies was crucial for the sustenance of culture change.
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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 | 0.036 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".