Quantifying the Length of Stay and Economic Impact of Albuterol and Levalbuterol in Hospitalized Patients With Chronic Obstructive Pulmonary Disease: A Retrospective Cohort Study
Bibliographic record
Abstract
Introduction Chronic obstructive pulmonary disease (COPD) affects millions in China and imposes a considerable economic burden on hospitalized patients who experience exacerbations. Nebulized short-acting beta-2 agonists (SABA) are recommended as initial therapy for exacerbation patients, but the optimal SABA remains uncertain. This study aimed to evaluate the impact of different SABAs, such as albuterol and levalbuterol, on the length of stay (LOS) and direct medical costs among hospitalized patients diagnosed with COPD. Methods This retrospective cohort study uses linked hospital administrative data from three hospitals in Chongqing. Patients with COPD, aged 40 years and older, who had been continuously treated with nebulized albuterol or levalbuterol during hospitalization, were eligible for the study. Patients were matched 1:1 by sex, age, and severity according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) grades 1-4. Patients were grouped according to the different SABA treatments they received. Demographic, economic, and clinical data were retrieved. LOS and direct healthcare costs were assessed. Results A total of 158 COPD patients were included, with 79 in each treatment group. Patients treated with levalbuterol had a significantly shorter median LOS (7.0 days vs. 8.0 days, P=0.003) and fewer direct healthcare median costs (total cost: ¥8,868.3 vs. ¥10,290.7, P=0.014; COPD-related western medicine fees: ¥383.8 vs. ¥505.3). Patients aged 60 or older were more likely to experience longer LOS and higher direct costs. Conclusion This retrospective cohort analysis supports that albuterol was associated with longer LOS and higher costs than levalbuterol.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".