Identification of latent classes of randomized controlled trials based on integrity and reporting characteristics
Bibliographic record
Abstract
OBJECTIVES: Reliable, well-conducted, and adequately reported research is essential for decision-making. This study uses an exploratory clustering approach to identify patterns (subgroups) of trials based on research conduct and reporting characteristics to better understand heterogeneity. Describing features of these subgroups may help elucidate mechanisms of poor planning and reporting that can be acted upon by the research community to improve the research practices. Estimating the impact of trials from specific subgroup classes on pooled treatment effects and overall certainty of evidence may inform conduct and interpretation of systematic reviews in the future. STUDY DESIGN AND SETTING: Our study used data from 456 randomized controlled trials eligible for our recent update of a Cochrane review, Exercise treatment for chronic low back pain, to explore groups of trials that have various characteristic patterns of research planning, conduct, reporting, and publication. RESULTS: Using latent class analysis, we explored the patterns that exist in 43 characteristics of trial planning, conduct, reporting, and publication characteristics. We identified a 4-class model as the best fit for the data; classes were labeled based on the patterns of characteristics that emerged: (1) Well Resourced & Thorough, 155 trials (34%); (2) Dated, 92 trials (20%); (3) Fundamental Deficiencies, 102 trials (22%); and (4) Research Waste, 107 trials (24%). The characteristics that best differentiated the classes were trial registration status, institution/ethics board approval, conflict of interest reported, and reporting of adverse events. There were no significant differences for estimates of treatment effect in the four classes for all comparisons of treatments with pain intensity outcomes; two classes overestimated functional limitations outcomes for exercise compared to no trial treatment and compared to other conservative treatments. CONCLUSION: Using a single characteristic to define quality or trustworthiness is no longer sufficient; individual 'problematic' characteristics could be found in all four classes. This novel research could be used as a framework for further exploration of research integrity issues in the health sciences, and the development of interventions to improve our evidence-base, taking into consideration the research motivations and means of trialists.
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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 | 0.594 | 0.817 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.018 | 0.033 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.012 | 0.012 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 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".