Hemosiderin in sputum macrophages may predict infective exacerbations of chronic obstructive pulmonary disease: a retrospective observational study
Bibliographic record
Abstract
BACKGROUND: Infective exacerbations of COPD are common and are accompanied by neutrophilic bronchitis in sputum. Increased respiratory iron content has been associated with respiratory tract infection, though it is unclear if this represents a predisposing factor for infection or the sequelae of inflammation. Iron overload, as assessed in the airways, may be an important biomarker for recurrent infective exacerbations of COPD. The purpose of our study was to determine if hemosiderin in sputum macrophages is related to infective exacerbations of COPD. METHODS: We undertook a retrospective observational study of 54 consecutive patients who presented with an exacerbation of COPD and had sputum examined including assessment for hemosiderin in alveolar macrophages. The relation between infective exacerbations in the previous two years and the percent of hemosiderin-positive macrophages was analyzed with linear regression. To account for the non-parametric distribution of infective exacerbations, negative binomial regression modelling was used to account for other covariates. RESULTS: The percent of hemosiderin positive alveolar macrophages (hemosiderin index), analyzed parametrically and non-parametrically, demonstrated a significant correlation with increasing numbers of infective exacerbations in the previous two years. In a multivariate regression analysis, hemosiderin index was an independent predictor of infective exacerbations. COPD patients with raised hemosiderin index (≥20%) had higher levels of sputum IL-6 compared to patients with lower levels (<20%). CONCLUSIONS: High hemosiderin index in sputum alveolar macrophages measured at the time of AECOPD may be related to the frequency of infective exacerbations of COPD.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".