Impact of Undiagnosed Chronic Obstructive Pulmonary Disease and Asthma on Symptoms, Quality of Life, Healthcare Use, and Work Productivity
Bibliographic record
Abstract
Abstract Rationale A significant proportion of individuals with chronic obstructive pulmonary disease (COPD) and asthma remain undiagnosed. Objectives The objective of this study was to evaluate symptoms, quality of life, healthcare use, and work productivity in subjects with undiagnosed COPD or asthma compared with those previously diagnosed, as well as healthy control subjects. Methods This multicenter population-based case-finding study randomly recruited adults with respiratory symptoms who had no previous history of diagnosed lung disease from 17 Canadian centers using random digit dialing. Participants who exceeded symptom thresholds on the Asthma Screening Questionnaire or the COPD Diagnostic Questionnaire underwent pre- and post-bronchodilator spirometry to determine if they met diagnostic criteria for COPD or asthma. Two control groups, a healthy group without respiratory symptoms and a symptomatic group with previously diagnosed COPD or asthma, were similarly recruited. Measurements and Main Results A total of 26,905 symptomatic individuals were interviewed, and 4,272 subjects were eligible. Of these, 2,857 completed pre- and post-bronchodilator spirometry, and 595 (21%) met diagnostic criteria for COPD or asthma. Individuals with undiagnosed COPD or asthma reported greater impact of symptoms on health status and daily activities, worse disease-specific and general quality of life, greater healthcare use, and poorer work productivity than healthy control subjects. Individuals with undiagnosed asthma had symptoms, quality of life, and healthcare use burden similar to those of individuals with previously diagnosed asthma, whereas subjects with undiagnosed COPD were less disabled than those with previously diagnosed COPD. Conclusions Undiagnosed COPD or asthma imposes important, unmeasured burdens on the healthcare system and is associated with poor health status and negative effects on work productivity.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".