Outcomes in exercise-based interventions in interstitial lung diseases
Bibliographic record
Abstract
The effect of exercise-based interventions, such as pulmonary rehabilitation, in interstitial lung diseases (ILD) is unclear. Reasons include inadequate reports of studies’ methods, which make the interpretation of results across trials challenging. A core outcome set to be used in clinical trials enrolling patients with ILD was published in 2014 (Saketkoo et al. Thorax, 2014, 69.5: 436-44). However, its use by trials in exercise-based interventions is unknown. We reviewed the outcomes most used in clinical trials exploring exercise-based interventions in ILD. Pubmed, Web of Science, Scopus and EBSCO were searched until August 2019. Randomized controlled trials exploring the effects of exercise-based interventions in patients with ILD were included. Title, abstract and full text were screened by 2 researchers independently and consensus was reached. The search strategy resulted in 10010 possibly eligible articles. After comprehensive screening, 15 were withheld for data extraction. Patient-reported and clinical outcomes and measures found are in figure 1. Inconsistencies between the core outcome set and trials’ reports were found for the use of imaging (recommended-not used), exercise tolerance (used-not recommended) and cough (recommended-not used) outcomes and measures. A specific core outcome set for clinical trials exploring exercise-based interventions, including pulmonary rehabilitation, in patients with ILD may be needed.
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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 | 0.039 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".