Heterogeneity in the respiratory symptoms of patients with mild-moderate COPD
Bibliographic record
Abstract
ABSTRACT Background The burden of symptoms varies markedly between patients with Chronic Obstructive Pulmonary Disease (COPD) and is only weakly correlated with lung function impairment. While heterogeneity in lung function decline and exacerbations have been previously studied, the extent of heterogeneity in symptoms and the factors associated with this heterogeneity are not well understood. Methods A sample of the general Canadian population ≥40 years with persistent airflow limitation was followed for up to 3 years. Participants reported whether they experienced chronic coughing, phlegm, wheezing, or dyspnea during visits at 18-month intervals. We used mixed-effect logistic regression models (separately for each symptom) to assess overall heterogeneity in the occurrence of symptoms between individuals, and the proportion of variation in symptom burden explained by lung function versus all other clinical characteristics of participants. Results 548 participants (54% male, mean age 67 years) contributed 1,086 visits in total, and 82% of patients reported at least one symptom during follow-up. There was substantial heterogeneity in the individual-specific probabilities for the occurrence of symptoms. This heterogeneity was highest for dyspnea and lowest for phlegm (interquartile range of probabilities: 0.15-0.77 and <0.01-0.53, respectively). FEV 1 explained 82% of the variation between individuals in the occurrence of phlegm, 26% for dyspnea, 3% for cough, and <0.1% for wheeze. All clinical characteristics of participants (including FEV 1 ) explained between 86% of heterogeneity in the occurrence of phlegm to <1% for wheeze. Conclusion There is marked heterogeneity in the burden of respiratory symptoms between COPD patients. The ability of lung function and other commonly measured clinical characteristics to explain this heterogeneity differs between symptoms.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".