Sputum Inflammometry to Manage Chronic Obstructive Pulmonary Disease Exacerbations: Beyond Guidelines
Bibliographic record
Abstract
Quantitative sputum cytometry facilitates in assessing the nature of bronchitis associated with exacerbations of chronic obstructive pulmonary disease (COPD). This is not assessed in most clinical trials that evaluate the effectiveness of strategies to prevent or to treat exacerbations. While up to a quarter of exacerbations may be associated with raised eosinophil numbers, the vast majority of exacerbations are associated with neutrophilic bronchitis that may indicate airway infections. While eosinophilia may be a predictor of response to corticosteroids (oral and inhaled), the limited efficacy of anti-interleukin 5 therapies would suggest that eosinophils may not directly contribute to those exacerbations. However, they may contribute to airspace enlargement in patients with COPD through various mechanisms involving the interleukin 13 and matrix metalloprotease pathways. The absence of eosinophils may facilitate in limiting the unnecessary use of corticosteroids. The presence of neutrophiia could prompt an investigation for the specific pathogens in the airway. Additionally, sputum measurements may also provide insight into the mechanisms of susceptibility to airway infections. Iron within sputum macrophages, identified by hemosiderin staining (and by more direct quantification) may impair macrophage functions while the low levels of immunoglobulins in sputum may also contribute to airway infections. The assessment of sputum at the time of exacerbations thus would facilitate in customizing treatment and treat current exacerbations and reduce future risk of exacerbations.
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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| 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".