Characteristics of subjects susceptible to exacerbation-like respiratory events in a population-based cohort: Canadian cohort obstructive lung disease (CanCOLD) study
Bibliographic record
Abstract
Rationale: There is no information on the characteristics of subjects sampled from the general population with mild to moderate COPD who are susceptible to exacerbation-like respiratory events. Objective: Assess in a population-based sample, if there are differences between the characteristics of subjects with COPD who report exacerbation-like events and those who do not. Methods: Canadian Cohort Obstructive Lung Disease (CanCOLD) is a longitudinal, multi-center study, 1400 subjects, ≥40 years old, identified by random digit dialling from the general population. Subjects are sex- and age-matched, and grouped:1) COPD moderate+ (GOLD≥2); 2) COPD mild (GOLD1); 3) at risk (ever smoker); 4) healthy (never smoker, no obstruction). An exacerbation questionnaire is administered at baseline and every 3 months to detect changes in respiratory symptoms, medication, work and health service use. Results: In a preliminary analysis of 372 subjects with COPD, 73 reported having an exacerbation-like event during a 6-month follow-up, 299 did not. Subjects who reported at least 1 event had greater BODE scores, a 2-fold increase in respiratory medication, and decreased lung function and health status. Smoking, MRC dyspnea score and co-morbidities were not associated with exacerbation events. Frequency of exacerbations increased with disease severity. Conclusions: From a population-sampling based cohort that mirrors the population of COPD patients at large,exacerbation-like respiratory events are associated with worsening disease manifestation in subjects with mild or moderate COPD. Longitudinal evaluation will allow for phenotype analysis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| 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".