Ambient Temperature and the Risk of Renal Colic: A Population-Based Study of the Impact of Demographics and Comorbidity
Bibliographic record
Abstract
PURPOSE: To examine the impact of ambient temperature on the incidence of emergency department (ED) admissions for acute renal colic and the potential influence demographics and comorbid conditions may have on this. METHODS: We conducted a population-based time series analysis using linked healthcare databases in Ontario, Canada, which included all residents, aged ≥19 years, who were admitted to an ED from April 2002 to December 2013. The primary outcome was daily number of renal colic emergency department admissions. A distributed lag nonlinear model with 21 days of lag was applied to estimate the cumulative effect of temperature on colic admissions. We estimated risks for cold and heat, defined as temperatures below and above the optimal temperature, which corresponded to the point with minimum risk of colic admissions. We conducted stratified analyses using selected demographics and comorbidities. RESULTS: During the study period, 423,396 patients presented to an ED with colic. There was a significantly increased risk of colic as ambient temperature increased (rate ratio [RR] = 1.30, 95% confidence interval [CI]: 1.20, 1.42). Subgroup analysis demonstrated an increased risk associated with heat for both genders; however, this risk was more pronounced in males with extreme heat (RR = 1.64 vs 1.22, p = 0.006). In contrast to other age groups, there was an increased risk for those in their 40s (RR = 1.42), 50s (RR = 1.54), and 60s (RR = 1.31) (p = 0.02). CONCLUSION: Increasing ambient temperature was associated with increased risk of ED visits for colic, particularly in males and those aged 40 to 69 years.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".