Designed Delays Versus Rigorous Pragmatic Trials
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
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.
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.
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 | 0.032 | 0.729 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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