Seasonal variation in hospital encounters with hypoglycaemia and hyperglycaemia
Bibliographic record
Abstract
Abstract Aim To assess whether rates of hospital encounters with hypoglycaemia and hyperglycaemia display seasonal variation. Methods Time series analyses of the monthly rates of hospital encounters (emergency room visits or inpatient admissions) with hypoglycaemia and hyperglycaemia from 2003 to 2012 using linked healthcare databases in Ontario, Canada. Results Over the study period, there were 129 887 hypoglycaemia and 79 773 hyperglycaemia encounters. The characteristics of people at the time of their encounters were similar across the seasons in 2008 (median age 68 years for hypoglycaemia encounters and 53 years for hyperglycaemia encounters; 50% female; 90% with diabetes). We observed moderate seasonality in both types of encounters ( R 2 autoregression coefficient 0.58 for hypoglycaemia; 0.59 for hyperglycaemia). The rate of hypoglycaemia encounters appeared to peak between April and June, when on average, there was an additional 49 encounters per month (0.36 encounters per 100 000 persons per month) compared with the other calendar months (5% increase). The rate of hyperglycaemia encounters appeared to peak in January, when on average, there was an additional 69 encounters per month (0.50 encounters per 100 000 persons per month) compared with the other calendar months (11% increase). Conclusions In our region, there is seasonal variation in the rate of hospital encounters with hypoglycaemia and hyperglycaemia. Our findings may help to highlight periods of vulnerability for people, may inform future epidemiological studies and may aid in the appropriate planning of healthcare resources.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".