Measuring exacerbations in subjects with mild to moderate COPD from a population-based cohort: The CanCOLD study
Bibliographic record
Abstract
Rationale: There is no information on COPD exacerbations in subjects with mild to moderate COPD sampled from the general population. Objective: To assess in a population-based sample the frequency and characteristics of COPD exacerbations. Methods: Canadian Cohort Obstructive Lung Disease (CanCOLD) is a longitudinal, multi-center study, 1600 subjects ≥ 40 years old, identified by random digit dialling from the general population. Subjects are sex- and age-matched, and grouped as: 1) COPD moderate + (GOLD≥ 2); 2) COPD mild (GOLD 1); 3) subjects at risk (ever smoker); 4) healthy subjects (never smoker, no obstruction). An exacerbation questionnaire is administered at baseline and every 3 months to capture changes in respiratory symptoms, medication, work and health service use. Results: From a preliminary analysis of 182 subjects, 35 were normal, 30 at risk, 62 GOLD 1 and 55 GOLD ≥2. Exacerbations at baseline were reported by 2 at risk, 2 GOLD 1 and 9 GOLD ≥2 subjects, suggesting a prevalence of 9% in this cohort. GOLD 1 and at-risk subjects experienced similar worsened respiratory symptoms, but no change in medication, work or health service use. GOLD ≥2 subjects experienced more symptoms, and changes in medication, work and health service use. Conclusions: Based on these preliminary data, COPD exacerbations are reported in a minority of COPD subjects sampled from the population. Exacerbations occur, and are similar in at-risk and GOLD 1 subjects, however in GOLD ≥2 subjects they are more common and have a greater impact. Longitudinal evaluation will be of great value.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".