Characteristics of Dutch and Swiss primary care COPD patients - baseline data of the ICE COLD ERIC study
Bibliographic record
Abstract
INTRODUCTION: INTERNATIONAL COLLABORATIVE EFFORT ON CHRONIC OBSTRUCTIVE LUNG DISEASE: Exacerbation Risk Index Cohorts (ICE COLD ERIC) is a prospective cohort study with chronic obstructive pulmonary disease (COPD) patients from Switzerland and The Netherlands designed to develop and validate practical COPD risk indices that predict the clinical course of COPD patients in primary care. This paper describes the characteristics of the cohorts at baseline. MATERIAL AND METHODS: Standardized assessments included lung function, patient history, self-administered questionnaires, exercise capacity, and a venous blood sample for analysis of biomarkers and genetics. RESULTS: A total of 260 Dutch and 151 Swiss patients were included. Median age was 66 years, 57% were male, 38% were current smokers, 55% were former smokers, and 76% had at least one and 40% had two or more comorbidities with cardiovascular disease being the most prevalent one. The use of any pulmonary and cardiovascular drugs was 84% and 66%, respectively. Although lung function results (median forced expiratory volume in 1 second [FEV(1)] was 59% of predicted) were similar across the two cohorts, Swiss patients reported better COPD-specific health-related quality of life (Chronic Respiratory Questionnaire) and had higher exercise capacity. DISCUSSION: COPD patients in the ICE COLD ERIC study represent a wide range of disease severities and the prevalence of multimorbidity is high. The rich variation in these primary care cohorts offers good opportunities to learn more about the clinical course of COPD.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".