Designed Delays Versus Rigorous Pragmatic Trials
Bibliographic record
Abstract
BACKGROUND: Centralized administrative databases enable low-cost pragmatic randomized trials (PRTs) of drug effectiveness and safety. We simplified the PRT strategy by using designed delays (DD) to evaluate drug policies. OBJECTIVES: To reassess our DD trial of a cost-saving nebulizer-to-inhaler conversion policy and a proposed DD trial of reduced restrictions on Cox-2 inhibitors. RESEARCH DESIGN: We randomized 52 pairs of communities and clusters of physician practices to the policy either on time or after a 6-month delay. Our 2-stage qualitative reassessment comprised: (1) applying criteria for reporting PRTs and (2) assessing DD trials in 3 domains of responsibility: policymakers' decisions, researchers' decisions, and joint decisions involving negotiation. MEASURES: A draft checklist of 22 Consolidated Standards of Reporting Trials (CONSORT). Researchers' recollections of their degree of influence on decisions. RESULTS: DD trials deviated from ideal PRTs in the policymakers' domain: the policies affected mixtures of drugs, users, and illnesses, and implementation was not by strict protocol. Aspects negotiated by researchers and policymakers also deviated from ideal: length of delay; size and location of control group; unit of randomization; additional data collection; and communications to physicians. The DD trials complied better with CONSORT in the researchers' domain of analysis and interpretation. CONCLUSIONS: DD trials can be negotiated with policymakers. Low cost and simplicity of DD trials partly compensate for some limitations for evaluating drug safety and effectiveness. The ethics question of whether a DD is routine evaluation or research depends on its purpose and generalizability.
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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 | 0.724 | 0.818 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".