Population-based analysis of the effect of a comprehensive, systematic change in an emergency medical service resource allocation plan on 24 hour mortality
Bibliographic record
Abstract
IntroductionThe British Columbia Emergency Health Services developed a new resource allocation plan (RAP) using an evidenced informed methodology and with further clinical input from EMS physicians, paramedics and allied EMS providers. Population-based analysis was used to determine the effect by comparing 24-hour mortality before and after province-wide implementation of the revised RAP. Objectives and ApproachThe primary outcome, 24-hour mortality, was obtained through linked provincial health administrative data. All adult cases with evaluable outcome data were included in the analysis. A pre and post methodology was used to evaluate the effect of post-RAP revision (post-RAP-revision) on 24-hour mortality compared to pre-RAP revision (pre-RAP-revision). Multivariable logistic regression was used to adjust for variations in other significant factors associated with 24-hour mortality. The interrupted time series (ITS) estimated any immediate changes in the level or trend of outcome after the start of the revised RAP implementation (fall of 2013), while simultaneously controlling for pre-existing trends. ResultsThe linked data set included some 1500–2000 children per school census classified as having a CP, representing a prevalence of some 0.3%. Provisionally, results show: prevalence of CP is higher amongst children living in relatively deprived areas; around 60% of CP children have a statement of SEN; the SEN type most commonly recorded for CP children with SEN is ‘Physical and medical difficulties’ and relatively high proportions have profound, multiple or severe learning difficulties; around 30% of CP children are educated in special schools; CP children in main stream (primary, middle and secondary) schools tended to miss more school sessions (~50% more) than other children and lower percentages achieved the expected levels at key stages 2 and 3 and the Level 2 GCSE threshold. Conclusion/ImplicationsOur results demonstrate that a comprehensive, evidence informed reconstruction of a provincial EMS RAP is feasible. Despite considerable change in crew level response and resource allocation, there was significant decrease in 24 hour mortality in a large pan-provincial population based patient cohort.
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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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".