Practical Considerations for Adaptive Trial Designs and Master Protocols
Bibliographic record
Abstract
In this chapter, we review practical considerations for adaptive trial designs and master protocols. Planning adaptive trial designs and master protocols require resources and time. It is best to plan ahead with key stakeholders with statistical, content, and operational expertise to make the trial possible. Customised education and training plans will likely be required for the vendors, investigators, and other personnel involved in the trial. Critical thinking is needed from the personnel involved to create flexible technology systems and procedures required to execute these clinical trials. During the conduct, it is important to document what happened, maintain a proper firewall, and manage external communications effectively. For long-term platform trials, study adjustments may be unavoidable, but it is important that these adjustments are made before the patients are enrolled into the new study arm.
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 | 0.458 | 0.613 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.014 | 0.035 |
| Insufficient payload (model declined to judge) | 0.021 | 0.012 |
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".