Quantitative sputum cell counts as a marker of airway inflammation in clinical practice
Bibliographic record
Abstract
PURPOSE OF REVIEW: Bronchitis, meaning airway inflammation, is an important component of airway disease. Yet respirologists and allergists, who have stressed the importance of measurements of airway function, have been slow to introduce airway inflammation measurements into clinical practice. Of the measurements available, quantitative sputum cell counts have the most clinical value. This article provides additional information on this topic from studies published in 2005 and 2006. RECENT FINDINGS: Airway diseases are heterogeneous within patients in terms of the disease present and the type of airway inflammation. Quantitative sputum cell counts (total cell count as well as the differential) identify noneosinophilic, mainly neutrophilic, probably infective exacerbations as common in patients with asthma and chronic obstructive pulmonary disease that may be unresponsive to corticosteroid treatment. In contrast, measurements of sputum eosinophils can be used to guide the minimum dose of corticosteroid required to control eosinophilic bronchitis and reduce eosinophilic exacerbations. SUMMARY: Measurements of quantitative sputum cell counts need to be made available, initially by tertiary care centres, to diagnose bronchitis in airway disease and to optimize treatment. Examination of how these are complemented by indirect measures of airway inflammation, specifically exhaled nitric oxide and airway hyperresponsiveness to stimuli acting indirectly through mediator release, requires further investigation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".