Methodological gaps in the assessment of risk minimization interventions: a systematic review
Bibliographic record
Abstract
INTRODUCTION: Since the introduction of therapeutic risk management regulatory guidance, an increase in the number of risk minimization interventions (RMIs) published in the literature has been observed. Methods used to evaluate their effectiveness remain, however, poorly examined. OBJECTIVE: This paper aimed to conduct a literature review on the methods of evaluation of effectiveness of RMIs and to identify methodological gaps. METHODS: The search was conducted using MEDLINE and Embase between 1 January 2000 and 31 December 2010, and updated on 1 April 2013. The following characteristics were extracted from each study: target population for the RMI, target population for the assessment of effectiveness, study design, data sources, and effectiveness outcome(s). RESULTS: A total of 188 unique RMIs were identified in the literature, of which effectiveness was evaluated in only 65 (34.6%) at the time of publication. The largest proportion of studies reviewed (n = 49, 75.4%) attempted to evaluate changes in behavior through prescribing or laboratory test practices. One quarter of studies evaluated the effect of RMIs on the occurrence of adverse events. Only a minority of studies used robust designs, such as randomized controlled trials (n = 6, 9.2%) or a quasi-experimental design with a parallel comparison group (n = 8, 12.3%). CONCLUSION: Lack of robust methodological design used in published studies on RMI effectiveness evaluation is an important methodological gap in the evaluation of RMI effectiveness. © 2014 The Authors. Pharmacoepidemiology and Drug Safety published by John Wiley & Sons, Ltd.
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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 | 0.038 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".