Lessons to be Learned for Gastroenterology from Recent Issues in Clinical Trial Methodology
Bibliographic record
Abstract
Randomized trials are the preferred tool for patient-oriented research, and their main role is to enable the transfer of results from basic research to routine application. While the need for randomized trials is evident, conducting these trials is becoming increasingly difficult and complex. This article reviews actual and conflicting issues of clinical trials with respect to gastroenterology. Major problems in trial design are neglect of previous research, inadequate sample size calculations and irrelevant outcome criteria. Significant trial management problems include subversion of random allocation, and the design of systems and procedures that are inefficient, ineffective and inflexible. One of the major challenges in conducting randomized, controlled trials is obtaining informed consent because of the differing perspectives and languages of physicians and patients. Recommendations include practical guidance in obtaining informed consent, feedback of trial results to patients and support of research related to obtaining informed consent. Despite statistical guidance, several critical issues persist with respect to trial analysis. The use of confidence intervals is under-represented, the presentation of baseline data is often omitted and postsubgroup analysis is performed. Another controversial but relevant issue is the intention-to-treat analysis. Despite the formulation of standards, there is consistently poor quality of trial reporting, poor registration of unpublished trials and limited registration of ongoing trials. The authors conclude that there is a need for more randomized trials in gastroenterology. While the complexity of trial conduction has increased, so have the means of methodological and practical support. Thus, all problems can be professionally tackled, resulting in good clinical research.
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 | 0.580 | 0.638 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.004 | 0.068 |
| Scholarly communication | 0.026 | 0.038 |
| Open science | 0.009 | 0.011 |
| Research integrity | 0.025 | 0.073 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".