Massive Hemorrhage Protocol adoption and standardization with a provincial toolkit: a follow-up survey of Ontario hospitals
Bibliographic record
Abstract
PURPOSE: Massive Hemorrhage Protocols improve outcomes for adults with severe hemorrhage, yet only 65% of Ontario hospitals had implemented one by 2018. In response, a Massive Hemorrhage Protocol toolkit was developed and disseminated province-wide in 2021. This study compares Massive Hemorrhage Protocol adoption and content in Ontario hospitals in 2023 versus 2018 using a pre- and post-toolkit rollout survey. METHODS: A 98-question survey was emailed to transfusion medicine laboratory directors or their delegate at 159 hospitals in 2023, 2 years after a provincial Massive Hemorrhage Protocol toolkit rollout that included a 1-day virtual symposium. Results were compared with the 2018 survey containing 82 identical core questions using Chi-square test, Fisher exact test, and Wilcoxon rank-sum nonparametric tests for quantitative data, and content analysis for qualitative data. RESULTS: The 2023 survey achieved a 100% response rate (n = 159); most respondents (n = 156) were transfusion staff. Hospitals with a Massive Hemorrhage Protocol increased significantly from 65% (n = 150) in 2018 to 77% (n = 159) in 2023 (p = 0.02). Small transfusion hospitals (< 5000 red blood cell units transfused/year) saw an increase in Massive Hemorrhage Protocol adoption from 60 to 74% (p = 0.02). By 2023, 95% (n = 159) of hospitals had/were implementing a Massive Hemorrhage Protocol. However, gaps in alignment to evidence-based recommendations remained, including hypothermia monitoring (missing in 25% of Massive Hemorrhage Protocols) tranexamic acid dosing (missing in 19%), and quality metric tracking (missing in 55%). Pediatric content was absent in 45% of Massive Hemorrhage Protocols in health centers caring for children. CONCLUSION: The provincial Massive Hemorrhage Protocol toolkit's dissemination was feasible and associated with increased adoption in Ontario hospitals. Two-years post rollout, 77% of provincial hospitals have Massive Hemorrhage Protocols in place. Opportunities remain to align contents with evidence-based recommendations and expand to remaining hospitals. This strategy could guide other jurisdictions to improve Massive Hemorrhage Protocol adoption and harmonize practices.
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".