Healthcare system encounters before COPD diagnosis: a registry-based longitudinal cohort study
Bibliographic record
Abstract
BACKGROUND: There is high interest in strategies for improving early detection of chronic obstructive pulmonary disease (COPD). These strategies often rely on opportunistic encounters between patients with undiagnosed COPD and the healthcare system; however, the frequency of these encounters is currently unknown. METHODS: We used administrative health data for the province of British Columbia, Canada, from 1996 to 2015. We identified patients with COPD using a validated case definition, and assessed their visits to pharmacists, primary care and specialist physicians in the 5 years prior to the initial diagnosis of COPD. We used generalised linear models to compare the rate of outpatient visits between COPD and non-COPD comparator subjects matched on age, sex and socioeconomic status. RESULTS: We assessed 112 635 COPD and non-COPD pairs (mean 68.6 years, 51.0% male). Patients with COPD interacted with pharmacists most frequently in the 5 years before diagnosis (mean 14.09, IQR 4-17 visits/year), followed by primary care (10.29, IQR 4-13 visits/year) and specialist (8.11, IQR 2-11 visits/year) physicians. In the 2 years prior to diagnosis, 72.1% of patients with COPD had a respiratory-related primary care visit that did not result in a COPD diagnosis. Compared with non-COPD subjects, patients with COPD had higher rates of primary care (rate ratio (RR) 1.40, 95% CI 1.39 to 1.41), specialist (RR 1.35, 95% CI 1.34 to 1.37) and pharmacist (RR 1.62, 95% CI 1.60 to 1.63) encounters. CONCLUSIONS: Patients with COPD used higher rates of outpatient services before diagnosis than non-COPD subjects. Case detection technologies implemented in pharmacy or primary care settings have opportunities to diagnose COPD earlier.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".