Effect of Ambient Temperature on Renal Colic: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Renal colic is a clinical manifestation characterized by spasmodic pain in the lower back, which is commonly caused by kidney stones. Although limited, recent evidence suggests increased risk of kidney outcomes in exposure to elevated ambient temperature. In this meta-analysis, we aimed to elucidate the effect of ambient temperature on renal colic. Methods: We systematically searched four leading bibliographic databases (PubMed, CINAHL complete, Scopus, and Web of Sciences) and additional sources from the inception of each database to February 12, 2024. Epidemiological studies that met a pre-determined eligibility criteria following PECOS (population, exposure, comparator, outcome, and study design) were included. The effect sizes from individual studies were standardized to Cohen’s d and we performed a meta-analysis with a random-effects model to estimate a pooled Cohen’s d and constructed a 95% confidence interval. We performed further sub-group analyses by study region, sample size, study design, and mean age of study participants. Results: Out of 1463 primarily retrieved articles, seven articles representing 758666 study participants underwent meta-analysis. Most of the studies were conducted in the US (3) followed by China (2), Canada (1), and Israel (1). Ambient temperature demonstrated a significant effect on renal colic [Cohen’s d: 0.30, 95% CI: 0.18, 0.41, p-value < 0.001, I2 = 99.08%]. Subgroup analyses for potential explanation of heterogeneity demonstrated statistically significant between-group variance for study region [America- Cohen’s d: 0.25, (95% CI: 0.11, 0.39); Asia - Cohen’s d: 0.44, (95% CI: 0.42, 0.45)] and study design [Case-crossover- Cohen’s d: 0.46, (95% CI: 0.01, 0.93); Retrospective cohort - Cohen’s d: 0.44, (95% CI: 0.42, 0.45); Time-series - Cohen’s d: 0.21, (95% CI: 0.17, 0.25)]. Conclusion: These findings suggest a moderate effect of ambient temperature on renal colic.Figure 1: Forest plot showing summary-effect.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.015 | 0.007 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".