COPD severity and health impact across the current CanCOLD population
Bibliographic record
Abstract
Introduction: Based on the observation that COPD prevalence from COLD is 4-fold higher than previous estimates, CanCOLD (Canadian Cohort Obstructive Lung disease) has been built to better characterize COPD subject phenotypes. Objective: To determine in a random population sampling of non institutionalized adults aged ≥40 years the severity of COPD detected with spirometry and the impact on health. Methods: CanCOLD is a prospective longitudinal cohort study (9 sites), tracking 1800 subjects with assessment at baseline, 18 and 36 months. CanCOLD sampling is based on the selection and contact of COPD subjects from the prevalence study COLD. Then matched non-COPD peers are selected/contacted. Measurements are in 5 categories: questionnaires (SF-36, SGRQ and CAT); pulmonary function and exercise tests; Chest CT scan; blood tests; and administrative databases. Results: More than 25% (>400 subjects) recruitment is accomplished. There was no difference of the SF-36 scores for GOLD2+, GOLD1, at risk and healthy subjects. GOLD1 reported similar health status than at risk (SGRQ, CAT) and healthy subjects (CAT). GOLD2+ reported worsening health status compared to GOLD1, at risk (SGRQ, CAT) and healthy subjects (CAT). In subjects started by their physicians on any respiratory medication, the CAT scores [mean (SD)] were 12.9 (8.2), 9.9 (5.5), 7.5 (5.4) and 7.1 (5.3) for GOLD2+, GOLD1, at risk and healthy, and for those not on respiratory medication 7.9 (5.5), 5.3 (5.0), 5.7 (5.4) and 5.8 (3.9). Similar results were found with SGRQ. Conclusions: Clusters based on CAT and SGRQ can be of interest to phenotype COPD subjects in the population. Funding by CIHR RxD ClinicalTrials.gov: [NCT00920348][1]. [1]: /lookup/external-ref?link_type=CLINTRIALGOV&access_num=NCT00920348&atom=%2Ferj%2F40%2FSuppl_56%2FP3443.atom
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".