Update on Asthma–COPD Overlap (ACO): A Narrative Review
Bibliographic record
Abstract
Although chronic obstructive pulmonary disease (COPD) and asthma are well-characterized diseases, they can coexist in a given patient. The term asthma-COPD overlap (ACO) was introduced to describe patients that have clinical features of both diseases and may represent around 25% of COPD patients and around 20% of asthma patients. Despite the increasing interest in ACO, there are still substantial controversies regarding its definition and its position within clinical guidelines for patients with obstructive lung disease. In general, most definitions indicate that ACO patients must present with non-reversible airflow limitation, significant exposure to smoking or other noxious particles or gases, together with features of asthma. In patients with a primary diagnosis of COPD, the identification of ACO has therapeutic implication because the asthmatic component should be treated with inhaled corticosteroids and some studies suggest that the most severe patients may respond to biological agents indicated for severe asthma. This manuscript aims to summarize the current state-of-the-art of ACO. The definitions, prevalence, and clinical manifestations will be reviewed and some innovative aspects, such as genetics, epigenetics, and biomarkers will be addressed. Lastly, the management and prognosis will be outlined as well as the position of ACO in the COPD and asthma guidelines.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".