Late-breaking abstract: Cluster analysis of objectively measured physical activity in 1001 COPD patients
Bibliographic record
Abstract
Background: Detailed analyses of physical activity (PA) measures in chronic obstructive pulmonary disease (COPD) have been insufficiently explored. We aimed to identify clusters of COPD patients based on objectively measured PA data, and to compare clinical characteristics, PA measures and PA hourly patterns between these clusters. Methods: 1001 COPD patients (65% men; median age and FEV1: 67 yrs and 49%pred, respectively) from 10 countries were studied. PA measures and hourly patterns were analysed based on data from the multi-sensor Sensewear armband used for >=4 days. Principal component analysis was applied to PA data for dimensionality reduction, subsequently k-means cluster analysis was used to identify subgroups of COPD patients. Results: 5 clusters were identified (Table 1). ![Figure][1] Cluster 1 (very inactive) spent less time in moderate-to-vigorous intensity and more time in very light intensity, whilst cluster 5 (very active) presented an opposite behaviour. Cluster 1 also presented higher body mass index, lower FEV1 and worse dyspnoea compared to other clusters. PA hourly patterns revealed that in all clusters the peak of intensity occurred before midday, but also that more inactive clusters had a more similar pattern between week and weekend (Figure 1). Conclusions: Five subgroups of COPD patients were identified with distinct PA measures and hourly patterns. These findings may serve as a basis for tailored interventions in COPD. [1]: pending:yes
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".