What kind of randomised trials do patients and clinicians need?
Bibliographic record
Abstract
In 1967 Daniel Schwartz and Joseph Lellouch1 argued that there are 2 kinds of randomised controlled trials (RCTs) embodying radically different attitudes to evaluation of treatment. They named these trials “pragmatic” and “explanatory” and stated that these 2 attitudes require different approaches to the design of an RCT. The pragmatic attitude seeks to directly inform real-world decisions among alternative treatments, and Schwartz and Lellouch show that this purpose is satisfied in trials that test feasible interventions on typical patients in common settings, with usual care as the comparator, to widen real-world applicability. The explanatory attitude, in contrast, is directed to understanding a biological process by testing the hypothesis that the specified biological response is explained by exposure to a particular treatment. Tight restrictions on eligible participants, intense and closely monitored treatment, inactive control interventions (such as placebo), and an idealised healthcare setting maximise the comparison between intervention and control groups and increase the ability to test this kind of hypothesis. What attitude to RCT design is most useful for patients and clinicians? Clearly, the trial has to ask an important question that is relevant to some aspect of the care clinicians provide to their patients. The clinicians and patients in the trial should resemble the clinicians who are reading the trial report and the patients they typically treat. The intervention being evaluated in the trial should be deliverable by the clinician, and the outcome being used to judge whether the intervention is effective has to be something that the clinician and his or her patients recognise as being worth influencing. In short, the trial has to be applicable, or have what is often called external validity.2 Consider the NASCET trial.3 It asked the following question: among patients with symptomatic 70–99% stenosis of a carotid …
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Metaresearch Domain: Methods · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.475 | 0.755 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.024 | 0.007 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.006 | 0.043 |
| Scholarly communication | 0.033 | 0.086 |
| Open science | 0.013 | 0.013 |
| Research integrity | 0.057 | 0.034 |
| Insufficient payload (model declined to judge) | 0.021 | 0.016 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".