Clinical characteristics and healthcare resource utilization in patients with chronic obstructive pulmonary disease in Hong Kong
Bibliographic record
Abstract
OBJECTIVES: Chronic obstructive pulmonary disease (COPD) is a significant cause of mortality, with its prevalence projected to rise in Asia. The primary objective of this study was to describe clinical characteristics, maintenance treatment, and healthcare resource utilization (HCRU) among patients with COPD in Hong Kong. Secondary objectives were to assess patient demographics and clinical characteristics by eosinophil (EOS) levels, and compare the demographics, clinical characteristics, and treatment patterns of patients on multiple-inhaler triple therapy (MITT). METHODS: This study analyzed a cohort of patients with COPD who had entered a previously initiated prospective cohort study involving patients with COPD and/or asthma at the Prince of Wales Hospital between 2017 and 2019. RESULTS: Patients with COPD were enrolled (N = 220, mean age 74.3 years, 97 % male). Twelve months prior to baseline assessment, 66 % of patients were on MITT, 17 % on long-acting muscarinic antagonists (LAMAs)/long-acting beta-agonists (LABAs), and 7 % on inhaled corticosteroids (ICS)/LABA. Compared with ICS/LABA or LAMA/LABA, more patients on MITT experienced ≥1 exacerbation (26.7 %, 10.5 %, 39.7 %, respectively). Patients on MITT also had a higher mean (SD) COPD Assessment Test score (9.4 [5.9]) and modified Medical Research Council Dyspnea Scale score (1.7 [0.7]) and incurred the most COPD-related and total HCRU costs. Compared with patients with EOS ≤300 cells/μL, those with EOS >300 cells/μL had a higher number of exacerbations. CONCLUSIONS: Patients with COPD in Hong Kong treated with MITT presented more severe disease profiles and incurred higher costs. These data can be used for decision making in patients with moderate-to-severe COPD in Hong Kong.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".