Pitfalls in designing trials of functional dyspepsia: the ascent and demise of itopride
Bibliographic record
Abstract
The publication in this issue of Gut ( see page 10.1136/gut.2007.132449 ) of the results of the clinical trial programme evaluating itopride in functional dyspepsia (FD)1 needs to be applauded on two counts: Over the last 15 years, definite progress has been made in the design, execution and data analysis of FD treatment trials. The efficacy of a limited number of treatment options has also been established. There is evidence that proton pump inhibitors (PPIs), and to a lesser extent H2 receptor antagonists (H2RAs), are efficacious in a proportion of FD patients.2–5 Importantly, the presence of heartburn appears to be a predictor of response.2 As a result, there is debate as to whether this is due to the inclusion of patients with unrecognised gastro-oesophageal reflux disease (GORD) symptoms rather than “true” FD patients.6 Consequently, there is a school of thought that non-response to a PPI should be a diagnostic criterion for a diagnosis of FD. In a small proportion of Helicobacter pylori -positive FD patients, cure of the infection will lead to a sustained improvement in symptoms with an estimated number needed to treat to obtain one success of 14.4 5 7 There has been a longstanding interest in the use of prokinetics, especially cisapride, as a treatment for FD. Unfortunately many of the cisapride studies suffered from poor study design, and there is evidence that this tended to overestimate treatment efficacy.4 5 8 Furthermore, it is possible that part of the response seen with cisapride in FD may have been related to associated heartburn symptoms.8 A systematic review on the …
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 | 0.747 | 0.748 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.010 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.015 | 0.029 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".