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.
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.
How this classification was reachedexpand
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".