The effect of a multi-faceted quality improvement program on paramedic intubation success in the critical care transport environment: a before-and-after study
Bibliographic record
Abstract
INTRODUCTION: Endotracheal intubation (ETI) is an infrequent but key component of prehospital and retrieval medicine. Common measures of quality of ETI are the first pass success rates (FPS) and ETI on the first attempt without occurrence of hypoxia or hypotension (DASH-1A). We present the results of a multi-faceted quality improvement program (QIP) on paramedic FPS and DASH-1A rates in a large regional critical care transport organization. METHODS: We conducted a retrospective database analysis, comparing FPS and DASH-1A rates before and after implementation of the QIP. We included all patients undergoing advanced airway management with a first strategy of ETI during the time period from January 2016 to December 2021. RESULTS: 484 patients met the inclusion criteria during the study period. Overall, the first pass intubation success (FPS) rate was 72% (350/484). There was an increase in FPS from the pre-intervention period (60%, 86/144) to the post-intervention period (86%, 148/173), p < 0.001. DASH-1A success rates improved from 45% (55/122) during the pre-intervention period to 55% (84/153) but this difference did not meet pre-defined statistical significance (p = 0.1). On univariate analysis, factors associated with improved FPS rates were the use of video-laryngoscope (VL), neuromuscular blockage, and intubation inside a healthcare facility. CONCLUSIONS: A multi-faceted advanced airway management QIP resulted in increased FPS intubation rates and a non-significant improvement in DASH-1A rates. A combination of modern equipment, targeted training, standardization and ongoing clinical governance is required to achieve and maintain safe intubation by paramedics in the prehospital and retrieval environment.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".