<p>Temporal Trends Of Pharmacologic Therapies For Patients With Chronic Obstructive Pulmonary Disease In Alberta, Canada</p>
Bibliographic record
Abstract
Objectives: To describe the trends in pharmacologic treatment for patients newly diagnosed with chronic obstructive pulmonary disease (COPD) in Alberta, Canada. Methods: We linked Alberta health databases to identify patients aged ≥35 years with incident COPD between April 2010 and March 2017. Incident cases were defined as those who did not have a hospitalization or outpatient visit with COPD in the previous 2 years. Patients were categorized into two groups: 1) incident cases at a hospital and 2) incident cases at an outpatient clinic, and both were followed until death or being censored by 31 March 2018. Utilization of COPD medication for 30 days following incident event and adherence in maintenance therapy over time were reported. Results: The study included 33,169 patients with incident COPD (hospital: 9,089; outpatient: 24,080). In 18,666 (56.3%) patients starting medication within 30 days of the incident event (2010: 52.7%; 2016: 56.6%; p=0.002), SABA (60.5%) and LABA/ICS (41.6%) were most commonly used. ICS (without LABA) was used in 14.2% and was used as monotherapy in 4.5% of patients. The proportion of patients who initiated any ICS was similar (hospital: 56.7%; outpatient: 55.7%; p=0.194) and decreased in both settings over time (p<0.001). Drug adherence during the first year after the incident event was 54.3%, higher among hospital patients (66.5% vs 48.9%; p<0.001), and improved over time (2010: 53.4%; 2016: 57.4%; p<0.001). Conclusion: The initiation of and adherence to pharmacologic therapy for patients with COPD is low but improves over time. While SABA and LABA/ICS are most commonly used, ICS utilization decreases over time.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".