Characteristics and Principles of Master Protocols
Bibliographic record
Abstract
In this chapter, we discuss the key concepts, terminologies, and principles of master protocols. The term ‘master protocol’ is often misunderstood and misused. This term refers to a single overarching protocol document that is developed with the intention of evaluating multiple interventional hypotheses. ‘Master protocol’ itself does not refer to a specific type of clinical trial. There are three types of clinical trials that are conducted using the master protocol framework: platform trials, basket trials, and umbrella trials. While master protocols can naturally extend to adaptive trial designs, the use of adaptive trial designs is not a defining feature of master protocols nor of platform, basket, and umbrella trials. Master protocols implement common screening, trial systems, and standardised operating procedures across multiple trial institutions under one centralised governance model. In addition to statistical efficiencies, operational efficiencies can be gained by adopting the master protocol framework.
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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 | 0.138 | 0.220 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.018 | 0.029 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.014 | 0.014 |
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".