Current practices and policies regarding the use of high-flow nasal cannula on general pediatric inpatient wards in Canada
Bibliographic record
Abstract
Abstract Objectives High-flow nasal cannula (HFNC) oxygen therapy has become a common treatment for respiratory conditions in children. To our knowledge, no study has described practice patterns for HFNC on pediatric inpatient wards in Canada. The aim of this study was to survey current practices and policies regarding the use of HFNC on the ward in children’s hospitals in Canada. Methods We conducted a web-based survey of Pediatric Hospital Medicine section chiefs in major tertiary care hospitals in Canada. The primary outcome was the proportion of hospitals that use HFNC on the general pediatric ward. Secondary outcomes included indications for HFNC, initial and maximum flow rates, maximum FiO2, method of nutrition delivery while on HFNC, level of nursing and respiratory therapist care required, criteria for pediatric intensive care unit transfer, and subjective successes and challenges of implementing a ward-based HFNC policy. Results The section chief survey response rate was 100% (15/15). Eight centres (53%) allowed the use of HFNC outside of an intensive care setting. Six centres initiated HFNC on the ward, while two centres only accepted patients after HFNC had been initiated in an intensive care setting. Other practices and policies varied considerably from centre to centre. Conclusion Our study reveals that approximately half of tertiary children’s hospitals in Canada currently use HFNC on the ward and utilize a range of practices and policies. Other centres are considering implementation. Further research is needed to inform best practices for HFNC therapy, support stewardship of health care resources, and promote safe patient care.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".