Endurance Time During Constant Work Rate Cycle Ergometry in COPD: Development of an Integrated Database From Interventional Studies
Bibliographic record
Abstract
Introduction: 2022; 9[2]:252-265), we described a conceptual framework establishing exercise endurance's direct relationship to an individual with COPD's experience of physical functioning in daily life, and that increase in exercise endurance is a patient-centered, meaningful treatment benefit. We further proposed endurance time during constant work rate cycle ergometery (CWRCE) as a useful efficacy endpoint in clinical therapeutic intervention trials. In this current publication, we describe the process of assembling an integrated database of endurance time responses to interventions in COPD. Methods: We sought participant-level data from published studies incorporating CWRCE as an outcome measure. A literature search screened 2993 publications and identified 553 studies for assessment. Two interventions had sufficient data across studies to warrant data extraction: bronchodilators and rehabilitative exercise training. Investigators were contacted and requested to provide participant-by-participant data from their published studies. Results: The final dataset included data from 8 bronchodilator studies (2166) participants and 15 exercise training studies (3488 participants). The database includes 71 variables per participant, comprising demographic, pulmonary function, and detailed physiologic response data. This paper provides a detailed description of the analysis population, while analysis supporting the validation/qualification process and addressing other scientific questions will be described in subsequent publications.
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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 | 0.073 | 0.193 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.034 | 0.028 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".