ENSAT registry-based randomized clinical trials for adrenocortical carcinoma
Bibliographic record
Abstract
Adrenocortical carcinoma (ACC) is an orphan disease lacking effective systemic treatment options. The low incidence of the disease and high cost of clinical trials are major obstacles in the search for improved treatment strategies. As a novel approach, registry-based clinical trials have been introduced in clinical research, so allowing for significant cost reduction, but without compromising scientific benefit. Herein, we describe how the European Network for the Study of Adrenal Tumours (ENSAT) could transform its current registry into one fit for a clinical trial infrastructure. The rationale to perform randomized registry-based trials in ACC is outlined including an analysis of relevant limitations and challenges. We summarize a survey on this concept among ENSAT members who expressed a strong interest in the concept and rated its scientific potential as high. Legal aspects, including ethical approval of registry-based randomization were identified as potential obstacles. Finally, we describe three potential randomized registry-based clinical trials in an adjuvant setting and for advanced disease with a high potential to be executed within the framework of an advanced ENSAT registry. Thus we, therefore, provide the basis for future registry-based trials for ACC patients. This could ultimately provide proof-of-principle of how to perform more effective randomized trials for an orphan disease.
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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 | 0.019 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".