COPD 2024: Applying the Canadian Thoracic Society (CTS) 2023 Chronic Obstructive Pulmonary Disease (COPD) Guideline for Preventing Exacerbations, Improving Health Status, and Preventing Mortality
Bibliographic record
Abstract
Chronic Obstructive Pulmonary Disease (COPD) is a common, chronic respiratory condition that is associated with the risk of morbidity and mortality. Approximately 2 million Canadians live with COPD, and as many as 1 million suffer while remaining undiagnosed and untreated. COPD exacerbations represent the most expensive cause of hospitalization with the highest likelihood of hospital readmission. Exacerbations are the primary driver of mortality in patients with COPD. These exacerbations are the second leading cause of hospitalization in Canada with an average length of stay of 7 days. In fact, 1 in 5 patients with COPD will die within 1 year of their first hospitalization due to an exacerbation. For those 65 or older in Ontario, the overall 365-day mortality stands at nearly 28% following their first hospitalization due to an exacerbation. The best indicator of the risk of future exacerbations is a history of exacerbations. The time has come to end the stepwise pharmacologic escalation that has defined the treatment paradigm in COPD. The call to action is to shift from the slow promotion of inhaled pharmacotherapy based on exacerbations to a direct escalation to inhaled pharmacotherapy with demonstrated evidence to prevent the exacerbations.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
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".