Understanding the use and outcomes of high-flow nasal cannula among infants admitted to Canadian hospitals with bronchiolitis (CanFLO): a protocol for a multicentre, retrospective cohort study
Bibliographic record
Abstract
INTRODUCTION: Bronchiolitis is the most common viral lower respiratory tract infection in children under 2 years of age. Respiratory support with high-flow nasal cannula (HFNC) is increasingly used in this patient population with limited understanding of the patients most likely to benefit and considerable practice variability of use. This study aims to understand the factors associated with failure of HFNC support among patients with bronchiolitis and to describe the current practice variations of HFNC use in patients with bronchiolitis in Canadian hospitals including fluid management and parameters to initiate, escalate and discontinue HFNC support. METHODS AND ANALYSIS: This is a multicentre retrospective cohort study including hospitalised patients aged 0-24 months with bronchiolitis requiring support with HFNC between January 2017 and December 2021. Clinical data will be collected from patient medical records from Canadian hospitals (n=12), including academic and community centres. HFNC failure will be defined as the need for escalation to non-invasive or invasive mechanical ventilation. Factors associated with HFNC failure will be analysed using logistic regression. Descriptive statistics will be used to describe practice variations of HFNC utilisation and management. ETHICS AND DISSEMINATION: Approval from the Research Ethics Boards (REBs) has been obtained for each participating study site prior to onset of data collection including Clinical Trials Ontario for all Ontario hospital sites and REBs from British Columbia Children's Hospital, Stollery Children's Hospital, Montreal Children's Hospital and CHU Sainte-Justine. Study results will be disseminated through presentation at national/international conferences and publication in high-impact, peer-reviewed journals.
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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 | 0.025 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".