Monitoring response to treatment in asthma management: food for thought
Bibliographic record
Abstract
Asthma is a chronic inflammatory disorder of the airways that is characterized by episodic symptoms. In this regard, asthma management has classically involved periodic re-assessment by the health-care provider, during which therapy is altered mainly based on clinical and physiological parameters, such as assessment of symptoms, spirometry and peak expiratory flow monitoring. In this context, various markers of airway inflammation (e.g. eosinophils in the induced sputum, nitric oxide in the exhaled air) have been proposed to assess the severity of asthma and to adjust the therapy accordingly. The evaluation of airway hyper-responsiveness with different stimuli has also been suggested as a new tool to monitor asthma. However, the lack of definite relationships between airway inflammation and asthmatic symptoms strongly limit the use of markers of asthma severity in the clinical setting. Therefore, the need of new tools to assess the severity of asthma is raised. The ideal measurement employed to establish the proper asthmatic therapy should be safe, non-invasive, easy to perform, reproducible and accurate, and have the capability to monitor the changes induced by the therapeutic interventions. A careful review of the available techniques, and the evaluation of their sensitivity and specificity in the clinical setting is warranted.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".