The Therapeutic Effect of Extracellular Vesicles on Asthma in Pre-clinical Models: A Systematic Review Protocol
Bibliographic record
Abstract
Asthma is the most common chronic disease in children. It is characterized by difficulty in breathing and chronic airway inflammation associated with narrowing of the airways, and airway hyperresponsiveness. If left untreated, asthma can lead to respiratory distress and even death. A number of medications are available and prescribed to manage asthma. Yet despite that, only half of the asthmatic patients are able to control their condition. Extracellular vesicles (EVs) play an important role in transporting contents such as nucleic acids, proteins, and lipids to other cells. While EVs have been extensively studied as biomarkers of various pathological states, evidence indicates that they can play protective and therapeutic roles in mitigating diseases such as cancer, cardiovascular disease and asthma. Here we propose to conduct a systematic review that provides a detailed analysis of the therapeutic effect of EVs in mitigating the primary (inflammation, airway hyperresponsiveness) and secondary outcomes (airway remodelling, molecular indices of cellular signalling, and inflammatory mediators in serum) associated with asthma in preclinical studies.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".