Randomized Clinical Trials: The Meeting Place of Medical Practice and Clinical Research
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.
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 | 0.378 | 0.473 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.017 | 0.003 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.004 | 0.056 |
| Scholarly communication | 0.025 | 0.032 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.036 | 0.049 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".