Quantifying comorbidity in individuals with COPD: a population study
Bibliographic record
Abstract
Chronic obstructive pulmonary disease (COPD) has been associated with many types of comorbidity. We aimed to quantify the real world impact of COPD on lower respiratory tract infection, cardiovascular disease, diabetes, psychiatric disease, musculoskeletal disease and cancer, and their impact on COPD through health services. A population study using health administrative data from Ontario, Canada, in 2008-2012 was conducted. Absolute and adjusted relative rates of ambulatory care visits, emergency department visits and hospitalisations for the comorbidities of interest in people with and without COPD were determined and compared. Among 7 241 591 adults, 909 948 (12.6%) had COPD. Over half of all lung cancer, a third of all lower respiratory tract infection and cardiovascular disease, a quarter of all low trauma fracture, and a fifth of all psychiatric, musculoskeletal, non-lung cancer and diabetes ambulatory care visits, emergency department visits and hospitalisations in Ontario were used by people with COPD. Individuals with COPD used about five times more health services for lung cancer, and two times more health services for lower respiratory tract infections and cardiovascular disease than people without COPD. Individuals with COPD use a disproportionate amount of health services for comorbid disease, placing significant burden on the healthcare system.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".