Bibliographic record
Abstract
This chapter introduces clinical research concepts and randomised clinical trials, covering the basics and building blocks that are necessary to understand the topics of adaptive trial designs and master protocols. Clinical trials are a type of prospective experimental studies in which human volunteers receive specific interventions according to the research protocol, then are followed longitudinally over time. Clinical trials are typically conducted in a sequence (from phase I, phase IIA, phase IIB, and phase III) that builds on knowledge accumulated from non-clinical and previous clinical studies. Randomisation is a process of random assignment of clinical trial participants to one or more intervention group(s) or control group under comparison. The use of randomisation provides a sound basis for making statistical causal inference when estimating the comparative treatment effects between groups. Fixed sample trial design refers to a type of designs where the trial data is only analysed once when a priori determined sample size has been reached. Fixed sample trial designs are designed with a fixed maximum sample size, a fixed number of interventions, and a defined end to the trial. This is the most common approach to clinical trial research.
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 | 0.046 | 0.114 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.080 | 0.049 |
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".