Association of Obesity Prevalence and Ambient Temperature: A Systematic Review
Bibliographic record
Abstract
Introduction: Ambient temperature is considered to be an influential factor in metabolism, and reduced/increased ambient temperature to the thermoneutral zone (TNZ) (20.3-23°C for covered populations) lead to metabolic changes, while also affecting the prevalence of obesity. The present study aimed to review the findings on the correlation of ambient temperature with obesity in various regions with ambient temperature. Methods: This systematic review was conducted in July 2019 via searching in databases such as PubMed and Scopus using three terms to describe the exposure and four terms for the outcome. The quality of the articles was assessed using the Newcastle-Ottawa quality assessment. Among 461 selected articles, four cross-sectional studies were systematically reviewed. The quality of these studies was graded nine based on a nine-point scale. In addition, the four cross-sectional studies reported a correlation between the prevalence of obesity and ambient temperatures in various regions in Spain, Korea, England, and the United States. Results: An association has been reported between ambient temperature and obesity in various regions with ambient temperature, and increased ambient temperature to the TNZ has been associated with the higher prevalence of obesity, while higher temperature than the TNZ range has been reported to decrease the prevalence of obesity. Conclusion: Evidence suggests that ambient temperature may affect the prevalence of obesity. However, further investigations are required in different countries with wider temperature ranges in order determine the correlation between ambient temperature and the prevalence of obesity.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".