Understanding why restrictive trial eligibility criteria are inappropriate
Bibliographic record
Abstract
BACKGROUND: An important difference between explanatory and pragmatic clinical trials concerns eligibility criteria. Eligibility criteria are restrictive in explanatory trials, while pragmatic trials are more inclusive or even all-inclusive. METHODS: To better understand the diverging views regarding eligibility criteria, we examine the contrast between theoretical and clinical medicine, and 3 different research contexts: laboratory research, population studies and clinical trials. In each context we review the purpose for selecting study subjects or research material, as well as the type of inductive inference or generalization that is sought by such selection. RESULTS: In each context, selection concerns different things and serves different purposes: In the laboratory, selection concerns the homogenous research material that will help isolate a causal signal. In the epidemiological context selection concerns the (random) sampling method, designed to produce a representative sample of the population. In the clinical trial setting, selection concerns patients in need of care. Restrictive eligibility criteria become inappropriate in the care setting because the aim of the trial is not to represent a population nor to isolate a causal signal, but to find out which patients benefit from treatment. CONCLUSION: The idea of selecting patients comes from methods that belong to theoretical medicine. In the care setting, most clinical trials should be pragmatic and as inclusive as possible.
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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 | 0.438 | 0.653 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.015 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".