Effect of physical training on airway inflammation in bronchial asthma: a systematic review
Bibliographic record
Abstract
BACKGROUND: The majority of the global population cannot afford existing asthma pharmacotherapy. Physical training as an airway anti-inflammatory therapy for asthma could potentially be a non-invasive, easily available, affordable, and healthy treatment modality. However, effects of physical training on airway inflammation in asthma are currently inconclusive. The main objective of this review is to summarize the effects of physical training on airway inflammation in asthmatics. METHODS: A peer reviewed search was applied to Medline, Embase, Web of Science, Cochrane, and DARE databases. We included all observational epidemiological research studies and RCTs. Studies evaluating at least one marker of airway inflammation in asthmatics after a period of physical training were selected. Data extraction was performed in a blinded fashion. We decided a priori to avoid pooling of the data in anticipation of heterogeneity of the studies, specifically heterogeneity of airway inflammatory markers studied as outcome measures. RESULTS: From the initial 2635 studies; 23 studies (16 RCTs and 7 prospective cohort studies) were included. Study sizes were generally small (median sample size = 30). There was a reduction in C-reactive protein, malondialdehyde, nitric oxide, sputum cell counts and IgE in asthmatics with physical training. Mixed results were observed after training for fractional excretion of nitric oxide and bronchial hyperresponsiveness. The data was not pooled owing to significant heterogeneity between studies, and a funnel plot tests for publication bias were not performed because there were less than 10 studies for almost all outcome measures. Physical training intervention type, duration, intensity, frequency, primary outcome measures, methods of assessing outcome measures, and study designs were heterogeneous. CONCLUSION: Due to reporting issues, lack of information and heterogeneity there was no definite conclusion; however, some findings suggest physical training may reduce airway inflammation in asthmatics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.005 | 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".