Standardised early warning scores in rural interfacility transfers: A pilot study into their potential as a decision-making aid
Bibliographic record
Abstract
INTRODUCTION: While 12.4% of British Columbians live rurally, only 2.0% of specialists practise rurally, making interfacility transport of high-acuity patients vital. Decision-making aids have been identified as a way to improve the interfacility transfer process. We conducted a pilot study to explore the potential of the Standardised Early Warning Score (SEWS) as a decision-making aid for staff at sending facilities. METHODS: SEWSs were calculated from a database of 418 transfers from sending facilities in rural, small and medium population centres to larger receiving facilities. The SEWSs were compared against one another over time using McNemar's and the Wilcoxon signed-ranks tests. The SEWSs were then tested for their association with six outcomes using Pearson's or Fisher's Chi-squared test and the Mann-Whitney U-test. RESULTS: While at the sending facility, both the number of SEWSs that was four or greater and the average SEWS decreased over time (P < 0.001 for both). A first SEWS of four or greater was predictive of more intervention categories during transport (P = 0.047), an adverse event during transport (P = 0.004), an adverse event within 30 min of arrival at the receiving facility (P = 0.004) and death before discharge from the receiving facility (P = 0.043) but not deterioration during transport, or the length of stay at the receiving facility. CONCLUSION: Overall, the performance of the SEWS in the context of rural interfacility transport suggests that the tool will have utility in supporting decision-making.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".