Asociación entre la exposición al frío y el asma: revisión sistemática y meta-análisis, 1965-2015
Bibliographic record
Abstract
Objectives: to conduct a systematic review and a meta-analysis of observational and expe- rimental studies that explore the relation between asthma and cold exposure. Materials and methods: systematic review of experimental and observational studies pu- blished up to August 2015 in Pubmed, Embase and Lilacs. Two researchers selected studies that measured the occurrence of asthma in individuals exposed to different environmen- tal temperatures. A meta-analysis used RevMan 5.3’s random effects model to calculate a summary weighted Odds Ratio with 95% confidence intervals, and a sensitivity analysis identified the influence of each study. Subsequent subgroup analyses identified summary measures by type of cold exposure and study design. Additional analysis measured hetero- geneity and risk of bias. Results: we found 86 studies measuring the relation between cold exposure and asthma. We included 11.6% (10/86) of the studies in the meta-analysis and found an association between cold exposure and asthma with all the studies (ORw 2.0 95%CI 1.28-3.14), with the subgroup of experimental studies (ORw 3.8 IC95% 1.70-8.86), and with cold environmental air (ORw 1.59 IC95% 1.10-2.30). The studies had high risk of bias and statistical heteroge- neity [I2 : 63.1% (27%-81.4%)]. Conclusions: the results support the hypothesis of an association between asthma and cold exposure. This study encourages to explore the concepts proposed by traditional medi- cine to establish its benefits on prevention and care of respiratory diseases, such as asthma.
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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 | 0.031 | 0.054 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.040 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".