Readmission Risk in Chronic Obstructive Pulmonary Disease Patients: Comparative Study of Nebulized β2-Agonists
Bibliographic record
Abstract
BACKGROUND: Bronchodilators are used for managing the symptoms of chronic obstructive pulmonary disease (COPD) and minimizing the risk of hospitalization and readmission. Hospital readmission is predictive of morbidity and mortality. OBJECTIVE: -agonists (neb-SABA) following COPD-related hospitalization discharge. METHODS: This retrospective analysis utilized US-based pharmacy and medical claims records (2001-2011) to identify COPD patients aged ≥40 years receiving neb-LABA or neb-SABA treatment within 30 days following discharge from a COPD-related hospitalization. Patients had to be continuously enrolled in their health plan for ≥6 months before and after their first neb-LABA or neb-SABA prescription fill (index date), and adherent to the treatment for the first 3 months post-index date. To select patients with similar severity profiles, neb-LABA and neb-SABA patients were matched by baseline characteristics. Readmission risks were observed over the 6-month period following the index date and compared between neb-LABA and neb-SABA cohorts using the multiple variable Cox proportional hazards model. RESULTS: The analysis included 246 matched patients (neb-LABA = 123; neb-SABA = 123). The mean age was 67 years, and 54% were female. The average length of stay during index hospitalization was 4.4 days. After adjusting for potential confounders, the risk of readmission was 47% lower in the neb-LABA cohort than in the neb-SABA cohort (hazard ratio 0.53, 95% confidence interval 0.30-0.96; P = 0.0349). CONCLUSIONS: Patients receiving neb-LABAs had a significantly lower readmission risk within 6 months following a COPD-related hospitalization versus patients treated with neb-SABAs.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".