Evaluating the design and reporting of pragmatic trials in osteoarthritis research
Bibliographic record
Abstract
Objectives: Among the challenges in health research is translating interventions from controlled experimental settings to clinical and community settings where chronic disease is managed daily. Pragmatic trials offer a method for testing interventions in real-world settings but are seldom used in OA research. The aim of this study was to evaluate the literature on pragmatic trials in OA research up to August 2016 in order to identify strengths and weaknesses in the design and reporting of these trials. Methods: We used established guidelines to assess the degree to which 61 OA studies complied with pragmatic trial design and reporting. We assessed design according to the pragmatic-explanatory continuum indicator summary and reporting according to the pragmatic trials extension of the CONsolidated Standards of Reporting Trials guidelines. Results: None of the pragmatic trials met all 11 criteria evaluated and most of the trials met between 5 and 8 of the criteria. Criteria most often unmet pertained to practitioner expertise (by requiring specialists) and criteria most often met pertained to primary outcome analysis (by using intention-to-treat analysis). Conclusion: Our results suggest a lack of highly pragmatic trials in OA research. We identify this as a point of opportunity to improve research translation, since optimizing the design and reporting of pragmatic trials can facilitate implementation of evidence-based interventions for OA care.
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 | 0.911 | 0.963 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.014 | 0.018 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.013 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".