Prescribing Patterns for Long-Acting Inhaled Bronchodilators Among Rural Adults With Chronic Obstructive Pulmonary Disease
Bibliographic record
Abstract
Background: Chronic obstructive pulmonary disease (COPD) is prevalent in rural areas of the USA. Long-acting inhaled bronchodilators (LABDs) are a key tool in COPD management and are underutilized. The purpose of this study was to determine whether rates of prescriptions for LABD differed by payer among patients with COPD in a rural healthcare network. Methods: In analysis 1, a random sample of patients with spirometry- and symptom-confirmed COPD over April 1, 2017 to December 31, 2019 was identified. Patient characteristics, including payer status, extracted from medical records were compared for those who did and did not have any prescriptions for LABD during the study window. In analysis 2, patients with one or more COPD-related hospitalizations during the same time period were identified and similar comparisons were made by LABD prescription status. Results: Among a random sample of patients with spirometry-confirmed COPD, 93.0% had been prescribed LABD during the study window with no difference in proportion by payer. Among the 461 patients with a COPD-related hospitalization, 388 (84.2%) had been prescribed LABD, again with no difference in prescriptions by payer. Those with a COPD-related hospitalization who had been prescribed LABD were younger, had lower body mass index, were more likely to be current smokers and had higher rates of hospitalizations for COPD during the study period than those not prescribed LABD. Conclusion: While disparities in LABD utilization may occur due to cost or other barriers to filling prescriptions, in our study, prescriptions for LABD were common and did not differ by payer status.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".