Randomised controlled trials: important but overrated?
Bibliographic record
Abstract
Practising physicians individualise treatments, hoping to achieve optimal outcomes by tackling relevant patient variables. The randomised controlled trial (RCT) is universally accepted as the best means of comparison. Yet doctors sometimes wonder if particular patients might benefit more from treatments that fared worse in the RCT comparisons. Such clinicians may even feel ostracised by their peers for stepping outside treatments based on RCTs and guidelines. Are RCTs the only acceptable evaluations of how patient care can be assessed and delivered? In this controversy we explore the interpretation of RCT data for practising clinicians facing individualised patient choices. First, critical care anaesthetists John Boylan and Brian Kavanagh emphasise the dangers of bias and show how Bayesian approaches utilise prior probabilities to improve posterior (combined) probability estimates. Secondly, Jane Armitage, of the Clinical Trial Service Unit in Oxford, argues why RCTs remain essential and explores how the quality of randomisation can be improved through systematic reviews and by avoiding selective reporting.
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 | 0.694 | 0.879 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.018 | 0.007 |
| Bibliometrics | 0.013 | 0.020 |
| Science and technology studies | 0.005 | 0.031 |
| Scholarly communication | 0.022 | 0.034 |
| Open science | 0.015 | 0.010 |
| Research integrity | 0.033 | 0.029 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".