How to increase the value of randomised trials in COPD research
Bibliographic record
Abstract
Methodological criteria that increase the validity of randomised trials are often not considered in respiratory research, even in large chronic obstructive pulmonary disease (COPD) trials. We describe four important aspects in the design, analysis and reporting of randomised trials, selected based on their relevance to current COPD research and based on our judgments of importance for researchers and users of the literature. First, to optimally control for confounding, where confounding refers to a factor that is associated with an exposure or intervention and influences the outcome, a clear definition of the main relationship between treatment and the primary outcome as well as identification of measurable confounders is required. In addition to randomisation per se as the key method to protect against confounding, restriction (excluding patients with specific characteristics that may introduce confounding), stratification (separate randomisation of patients with specific characteristics) and statistical adjustment are means to be considered to optimally control for confounding that simple randomisation may not achieve. Secondly, the selection of the primary outcome should be guided by the importance to patients. Secondary outcomes provide hypotheses about the effects observed for the primary outcome and can provide important data for systematic reviews and meta-analyses, but should be interpreted with caution in single trials. Thirdly, in study power calculations, not only the actual sample size, but the number of events, has a large influence on the power of the study and, often, unrealistic assumptions about event rates are made to increase the feasibility of trials. Finally, essential steps to transfer results from research to practice include complete reporting of trials and developing tools, such as decision aids, to support patients and physicians in their shared decision making.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.713 | 0.938 |
| Meta-epidemiology (narrow) | 0.008 | 0.010 |
| Meta-epidemiology (broad) | 0.021 | 0.020 |
| Bibliometrics | 0.026 | 0.018 |
| Science and technology studies | 0.004 | 0.032 |
| Scholarly communication | 0.039 | 0.065 |
| Open science | 0.011 | 0.024 |
| Research integrity | 0.052 | 0.047 |
| Insufficient payload (model declined to judge) | 0.023 | 0.017 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".