Effect of physical training on airway inflammation in animal models of asthma: a systematic review
Bibliographic record
Abstract
BACKGROUND: There is little data on the effect of exercise on markers of airway inflammation in human asthmatics. The main objective of this review is to determine the effects of physical training on markers of airway inflammation in animal models of asthma. METHODS: A peer reviewed search was applied to Medline, Embase, Web of Science, Cochrane, and DARE databases. Data extraction was performed in a blinded fashion. RESULTS: From the initial 2336 studies, a total of 10 studies were selected for the final analysis. All were randomized controlled trials with low to moderate intensity training on ovalbumin-sensitized mice. In the exercised group of mice, there was a reduction in BAL eosinophils and Th-2 cytokines, no change in Th-1 cytokines, an increase in IL-10, and a reversal of airway remodeling. The data was not pooled owing to significant heterogeneity between studies, and a funnel plot test for publication bias was not performed because there were few studies reporting on any one outcome measure. The asthma models differed between studies in age and gender of mice, as well as in timing of physical training after sensitization. The risk of bias was unclear for some studies though this may not influence outcome measures. The accuracy of data extracted from graphics is unknown. CONCLUSIONS: Physical training improves airway inflammation in animal asthma models.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.006 |
| Bibliometrics | 0.006 | 0.006 |
| 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) | 0.005 | 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".