What kind of randomised trials do patients and clinicians need?
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
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 …
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.
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 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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