Protocol for a scoping review of post-trial extensions of randomised controlled trials using individually linked administrative and registry data
Bibliographic record
Abstract
INTRODUCTION: Well-conducted randomised controlled trials (RCTs) provide the least biased estimates of intervention effects. However, RCTs are costly and time-consuming to perform and long-term follow-up of participants may be hampered by lost contacts and financial constraints. Advances in computing and population-based registries have created new possibilities for increasing the value of RCTs by post-trial extension using linkage to routinely collected administrative/registry data in order to determine long-term interventional effects. There have been recent important examples, including 20+ years follow-up studies of trials of pravastatin and mammography. Despite the potential value of post-trial extension, there has been no systematic study of this literature. This scoping review aims to characterise published post-trial extension studies, assess their value, and identify any potential challenges associated with this approach. METHODS AND ANALYSIS: This review will use the recommended methods for scoping reviews. We will search MEDLINE, EMBASE and the Cochrane Central Register of Controlled Trials. A draft search strategy is included in this protocol. Review of titles and abstracts, full texts of potentially eligible studies and data/information extraction will be conducted independently by pairs of investigators. Eligible studies will be RCTs that investigated healthcare interventions that were extended by individual linkage to administrative/registry/electronic medical records data after the completion of the planned follow-up period. Information concerning the original trial, characteristics of the extension study, any clinical, policy or ethical implications and methodological or practical challenges will be collected using standardised forms. ETHICS AND DISSEMINATION: As this study uses secondary data, and does not include person-level data, ethics approval is not required. We aim to disseminate these findings through journals and conferences targeting triallists and researchers involved in health data linkage. We aim to produce guidance for investigators on the conduct of post-trial extensions using routinely collected data.
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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 | 0.158 | 0.247 |
| Meta-epidemiology (narrow) | 0.009 | 0.009 |
| Meta-epidemiology (broad) | 0.021 | 0.024 |
| Bibliometrics | 0.025 | 0.023 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.017 | 0.014 |
| Insufficient payload (model declined to judge) | 0.161 | 0.043 |
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 source (direct Gemma or distilled Codex), 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".