A perspective on point-of-care tests to detect eosinophilic bronchitis
Bibliographic record
Abstract
Approximately 50% of asthma exacerbations and a third of COPD exacerbations are associated with an eosinophilic bronchitis. Quantitative cell counts reliably identify the number of eosinophils in sputum and treatment strategies that are guided by sputum eosinophil counts lead to significantly better outcomes than strategies guided by conventional assessments of symptoms and airflow. However, cell counts are not widely available and the results are not available in real time. Similarly, more sophisticated detection methods using immunoassays or genetic analysis via polymerase chain reaction are too costly and thus not amenable to rapid point-of-care diagnosis. Blood eosinophil counts and fraction of exhaled nitric oxide correlate poorly with airway eosinophilia, particularly in patients with severe airway diseases who are on corticosteroid therapy. Point of care assessments of eosinophil-specific activity may be provided by breathomics that employ metabolomics profiling of volatile compounds in breath. However, it is too early to decide if this would provide quantitative data to monitor therapy and disease activities longitudinally. Herein we provide a perspective on the potential for developing simple point-of-care tests with special emphasis on the potential for a bio-active paper diagnostic test to quantitatively assay the amount of eosinophil peroxidase in sputum samples by employing different types of detection systems.
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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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".