The Impact of Respiratory Therapist Performed Point-of-Care Lung Ultrasound on the Respiratory Care in Neonates, Manitoba Experience, Canada
Bibliographic record
Abstract
Abstract Objective We aimed to evaluate the impact of the registered respiratory therapist (RRT) performed point-of-care lung ultrasound (POC-LUS) on patient management in the neonatal intensive care unit (NICU). Study Design This is a retrospective cohort study of neonates who had RRT performed POC-LUS in two level III NICUs in Winnipeg, Manitoba, Canada. The analysis aims mainly to describe the implementation process of the POC-LUS program. The primary outcome was the prediction of the change in clinical management. Results A total of 136 neonates underwent 171 POC-LUS studies during the study period. POC-LUS resulted in a change in clinical management following 113 POC-LUS studies (66%), while it supported continuing the same management in 58 studies (34%). The lung ultrasound severity score (LUSsc) was significantly higher in the group with worsening hypoxemic respiratory failure and on respiratory support than infants on respiratory support and stable or not on respiratory support, p < 0.0001. LUSsc was significantly higher in infants on either noninvasive or invasive than those not on respiratory support, p-value <0.0001. Conclusion RRT performed POC-LUS service utilization in Manitoba improved and guided the clinical management of a significant proportion of patients who received the service.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".