Randomized Clinical Trials: The Meeting Place of Medical Practice and Clinical Research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Randomized clinical trials (RCTs) have been used to assess interventions affecting health since biblical times. They provide the most valid means of measuring the true effects of intervention compared with no treatment or placebo. Although they can also be used to assess the value of diagnostic tests, this article focuses on randomized trials in the context of treatment. Key elements of an RCT include: the explicit definition of a clinically relevant question; the identification of an appropriate sample of patients; a clearly defined and reproducible intervention; an appropriate intervention and comparator; clinically relevant, measurable outcomes; and appropriate tools for measurement and analysis. It is also essential to establish that the question posed is ethical, the methods of study are valid, and that follow-up is complete, with an "intent to treat" analysis. Results are best presented using both proportions and absolute numbers. By providing clinical decision-makers with numbers needed to treat or harm, decisions may be better informed and easier to understand, than if proportions alone are used.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.919 | 0.929 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.018 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it