Hypoglycemia requiring paramedic assistance among adults in southwestern Ontario, Canada: a population-based retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: People with diabetes mellitus commonly experience hypoglycemia, but they may not necessarily present to hospital after severe hypoglycemia requiring paramedic assistance. We sought to describe the incidence and characteristics of calls for hypoglycemia requiring paramedic assistance among adults in southwestern Ontario, Canada, and to determine predictors of hospital transport. METHODS: This population-based retrospective cohort study used data extracted from ambulance call reports (ACRs) of 8 paramedic services of the Southwest Ontario Regional Base Hospital Program from January 2008 to June 2014. We described calls in which treatment for hypoglycemia was administered, summarized the incidence of hypoglycemia calls and performed logistic regression to determine predictors of hospital transport. RESULTS: Out of 470 467 ACRs during the study period, 9185 paramedic calls occurred in which hypoglycemia treatment was administered to an adult (mean age 60.2 yr, 56.8% male, 81.1% with documented diabetes). Refusal of hospital transport occurred in 2243 (24.4%) of calls. Documented diabetes diagnosis (adjusted odds ratio [OR] 0.82, 95% confidence interval [CI] 0.69-0.96), higher capillary blood glucose (adjusted OR 0.31, 95% CI 0.22-0.44) and overnight calls (adjusted OR 0.80, 95% CI 0.72-0.91) were associated with lower odds of hospital transport. Higher-acuity calls (adjusted OR 2.05, 95% CI 1.58-2.66) were associated with higher odds of transport. The estimated annual incidence rate of hypoglycemia requiring paramedic assistance was 108 per 10 000 people with diabetes per year. INTERPRETATION: Hypoglycemia requiring paramedic assistance in southwestern Ontario is common, and close to 25% of calls do not result in hospital transport. Physicians managing diabetes care may be unaware of patients' hypoglycemia requiring paramedic care, suggesting a potential gap in follow-up care; we suggest that paramedics play an important role in identifying those at high recurrence risk and communicating with their care providers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".