A survey of minimally invasive surfactant therapy in Canada
Bibliographic record
Abstract
Introduction: Minimally invasive surfactant therapy (MIST) can be used to treat neonatal respiratory distress syndrome in neonatal intensive care units (NICUs). Clinical and institutional variances in MIST utilization persist globally with little published research regarding MIST utilization in Canada. Therefore, the objective of this study was to survey MIST utilization in NICUs in Canada. Methods: (CNN) Evidence-based Practice for Improving Quality (EPIQ) Lung Health Group (LHG). Site demographics and surfactant therapy procedural details were categorically collected. Free text and multiple-choice questions were utilized to capture perceived barriers and individual preferences for MIST use. Results: Twenty-eight of 33 participating members of the CNN EPIQ-LHG completed the survey between April 2021 and October 2021 (85%); 17/28 (61%) respondents reported ongoing MIST utilization at their center. Most centers that used MIST techniques administered bovine lipid extract surfactant (68%), commonly using angiocatheters (47%) and purpose-built catheters (41%). MIST was widely used for patients at 26-33 weeks gestational age (88%). Nine centres had never used MIST (32%), and 3 indicated a plan to implement MIST within the next 2 years. Common barriers to MIST use included lack of consensus amongst clinicians (78%), lack of training (56%), and lack of experience with MIST (56%). Conclusion: While MIST is being increasingly used in Canadian NICUs, universal use is yet to be seen. Clinician inexperience and lack of consensus, formal training, and local guidelines contribute to underutilization of MIST. Training workshops, country-wide data collection, and uniform operating protocols are needed to standardize practice.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".