Seeing what you want to see in randomised controlled trials: versions and perversions of UKPDS data
Bibliographic record
Abstract
Randomised controlled trials are objective, free of bias, and produce robust conclusions about the benefits and risks of treatment, and clinicians should be trained to rely on them; so says the gospel of evidence based practice. In this article we argue, using the United Kingdom prospective diabetes study (UKPDS) as an example, that there is one stage in the conduct of a randomised controlled trial—the interpretation and dissemination ofresults—that is open to several biases that can seriously distort the conclusions. By bias, we mean the epidemiological definition: anything that systematically distorts the comparisons between groups. We will argue that certain biases arise when different stakeholders assign their individual values to the interpretation of the final results of randomised controlled trials. #### Summary points Randomised trials are subject to interpretation bias as shown by the example of the UK prospective diabetes study The UK prospective diabetes study shows no benefit on macrovascular end points in patients with type 2 diabetes treated with sulphonylureas or insulin over 10 years The study shows a clinically important benefit on macrovascular end points from metformin in patients with type 2 diabetes that seems somewhat independent of the drug's ability to lower blood glucose concentrations Nevertheless, many authors, journal editors, and the wider scientific community interpreted the study as providing evidence of the benefit of intensive glucose control Journal editors should be aware of this important potential bias and encourage authors to present their results initially with a minimum of discussion so as to invite a range of comments and perspectives from readers Until 1998, type 2 diabetes had been treated for over 25 years with drugs such as the sulphonylureas, insulin, and metformin. Only one well designed, prospective clinical trial had evaluated the effect of these drugs on the development of microvascular and macrovascular disease. This was the …
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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 | 0.790 | 0.954 |
| Meta-epidemiology (narrow) | 0.004 | 0.008 |
| Meta-epidemiology (broad) | 0.015 | 0.013 |
| Bibliometrics | 0.018 | 0.035 |
| Science and technology studies | 0.007 | 0.073 |
| Scholarly communication | 0.064 | 0.047 |
| Open science | 0.014 | 0.024 |
| Research integrity | 0.038 | 0.074 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".