Quality of Clinical Trials in Gastroenteropancreatic Neuroendocrine Tumours
Bibliographic record
Abstract
BACKGROUND: The heterogeneity of neuroendocrine tumours (NETs) makes interpretation of clinical trials in this disease challenging. Our aim was to review the quality of treatment trials in NETs in order to inform the design and reporting of future studies. METHODS: We identified studies by searching MEDLINE. We considered all phase II and III trials of systemic antineoplastic treatments published between 2000 and 2011. Information on trial design, study population, end points, statistical considerations and results was abstracted from each article using a standardized form. RESULTS: Seven phase III and 39 phase II trials were identified. The make-up of the study population was variable: only 24% of trials included patients with one type of tumour (pancreatic NET or carcinoid tumour), 41% included patients with both tumour types, and 35% of trials included other endocrine cancers. Disease progression at baseline was often not reported and was documented for all patients in 22% of the trials. The functional status of the tumour, tumour differentiation, and Ki67 index were reported in 35, 43, and 15% of trials, respectively. The primary end point was clearly defined in 72% of trials. Identifiable statistical design, and predefined sample size were reported in 74 and 61% of trials, respectively. Conflicts of interest and study sponsorship were reported in 46 and 85% of trials. CONCLUSIONS: The quality of the design and reporting of phase II/III NET trials, as described in other cancers, is poor. Future trials should include more homogenous patient populations while adhering to rigorous selection, reporting and interpretation of population and trial parameters.
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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 | 0.519 | 0.796 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.010 |
| Bibliometrics | 0.012 | 0.017 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".