"Pragmatic" clinical trials: from whose perspective?
Bibliographic record
Abstract
Over the past 40 years, methodologists have become increasingly enthusiastic about conducting “pragmatic” clinical trials, which aim to simulate real-world settings as much as possible.1 In contrast to explanatory trials that are conducted under idealised circumstances, successful pragmatic trials will have the intended benefit of directly informing healthcare decision making. We wholeheartedly support the notion that, to the extent possible, trials should inform real-world decisions. In this editorial, we will, however, argue that the current conceptualisation of pragmatic trials sometimes serves the needs of only a small proportion of healthcare decision makers. Furthermore, a truly pragmatic or practical trial requires that clinical trialists carefully define the real-world context to which they hope their results apply, and design their trials accordingly.2 Authors’ descriptions of pragmatic trials have varied slightly, but most agree that such trials should enrol all patients to whom healthcare providers might offer the intervention, allow clinicians to administer the intervention and co-interventions without restrictions, and measure patient-important outcomes. In this discussion, we will focus on one tenet of pragmatic trials: that these trials should include patients who do not take the intervention as prescribed, presumably in the same proportion as they are likely to be seen in the community. We will interpret the results of a recent self-described pragmatic trial of nortriptyline as an adjunct to nicotine replacement for smoking cessation3 from 3 perspectives. We show that although it may be pragmatic for some, it certainly isn’t for all healthcare decision makers. The policy-maker is an employee of a third-party payer, who must decide which pharmaceutical products his organisation will fund for its members. His organisation is currently deciding whether to approve nortriptyline as an adjunct to nicotine …
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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 | Theoretical or conceptual | medium |
| gpt | Metaresearch Domain: Methods · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | medium |
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 | 0.588 | 0.749 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.019 | 0.006 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.007 | 0.066 |
| Scholarly communication | 0.042 | 0.079 |
| Open science | 0.013 | 0.012 |
| Research integrity | 0.067 | 0.079 |
| Insufficient payload (model declined to judge) | 0.006 | 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, 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".