Development of indicators for avoidable emergency medical service calls by mapping paramedic clinical impression codes to ambulatory care sensitive conditions and mental health conditions in the UK and Canada
Bibliographic record
Abstract
OBJECTIVE: Paramedic assessment data have not been used for research on avoidable calls. Paramedic impression codes are designated by paramedics on responding to a 911/999 medical emergency after an assessment of the presenting condition. Ambulatory care sensitive conditions (ACSCs) are non-acute health conditions not needing hospital admission when properly managed. This study aimed to map the paramedic impression codes to ACSCs and mental health conditions for use in future research on avoidable 911/999 calls. DESIGN: Mapping paramedic impression codes to existing definitions of ACSCs and mental health conditions. SETTING: East Midlands Region, UK and Southern Ontario, Canada. PARTICIPANTS: Expert panel from the UK-Canada Emergency Calls Data analysis and GEospatial mapping (EDGE) Consortium. RESULTS: Mapping was iterative first identifying the common ACSCs shared between the two countries then identifying the respective clinical impression codes for each country that mapped to those shared ACSCs as well as to mental health conditions. Experts from the UK-Canada EDGE Consortium contributed to both phases and were able to independently match the codes and then compare results. Clinical impression codes for paramedics in the UK were more extensive than those in Ontario. The mapping revealed some interesting inconsistencies between paramedic impression codes but also demonstrated that it was possible. CONCLUSION: This is an important first step in determining the number of ASCSs and mental health conditions that paramedics attend to, and in examining the clinical pathways of these individuals across the health system. This work lays the foundation for international comparative health services research on integrated pathways in primary care and emergency medical services.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 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".