Undiagnosed Chronic Obstructive Pulmonary Disease Contributes to the Burden of Health Care Use. Data from the CanCOLD Study
Bibliographic record
Abstract
RATIONALE: Chronic obstructive pulmonary disease (COPD) remains undiagnosed in many individuals with persistent airflow limitation. These individuals may be susceptible to exacerbation-like respiratory events that consume health care resources. OBJECTIVES: To compare exacerbation-like respiratory events, event prevalence, and differences in the odds of using medication and/or health services between subjects with diagnosed and undiagnosed COPD. METHODS: Subjects sampled from the general population participating in the CanCOLD (Canadian Cohort Obstructive Lung Disease) study, with at least 12 months of exacerbation-event follow-up who were classified as having physician-diagnosed or undiagnosed COPD were assessed. Exacerbation-like respiratory events were captured using a questionnaire administered every 3 months. MEASUREMENTS AND MAIN RESULTS: A total of 355 subjects were undiagnosed and 150 were diagnosed with COPD. Undiagnosed subjects were less symptomatic and functionally impaired, had been prescribed fewer respiratory medications, and had better health status. The incidence of reported exacerbation-like events was higher in diagnosed subjects and increased in both groups with the severity of airflow obstruction. Although subjects with diagnosed COPD were more often prescribed medication for exacerbation events, health service use for exacerbation events was similar in both groups. CONCLUSIONS: Most subjects with COPD in Canada remain undiagnosed. These subjects are less symptomatic and impaired, which may partly explain lack of diagnosis. Although patients with undiagnosed COPD experience fewer exacerbations than those with diagnosed COPD, they use a similar amount of health services for exacerbation events; thus, the overall health system burden of exacerbations in those with undiagnosed COPD is considerable.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".