The RIMES Statement: A Checklist to Assess the Quality of Studies Evaluating Risk Minimization Programs for Medicinal Products
Bibliographic record
Abstract
INTRODUCTION: Pharmaceutical risk minimization programs involve interventions designed to support safe and appropriate use of medicines. Currently, information regarding the evaluation of these programs is not publicly reported in a standardized and transparent manner. To address this gap, we developed and piloted a quality reporting checklist entitled the Reporting recommendations Intended for pharmaceutical risk Minimization Evaluation Studies (RIMES). METHODS: Checklist development was guided by three sources: (1) a theoretical framework derived from program theory and process evaluation; (2) public health intervention design and evaluation principles; and (3) a review of existing quality reporting checklists. Two raters independently reviewed 10 recently published (2012-2016) risk minimization program evaluation studies using the proposed checklist. Inter-rater reliability of the checklist was assessed using Cohen's Kappa and Gwet's AC1. RESULTS: A 43-item checklist was generated. Results indicated substantial inter-rater reliability overall (κ = 0.65, AC1 = 0.65) and for three (key information, design and evaluation) of the four subscales (κ ≥ 0.64, AC1 ≥ 0.64). The fourth subscale (implementation) showed low reliability based on Cohen's Kappa, but substantial reliability based on the AC1 (κ = 0.17, AC1 = 0.61). CONCLUSIONS: The RIMES statement augments relevant elements from existing quality reporting guidelines with items that address aspects of intervention design, implementation and evaluation specific to pharmaceutical risk minimization programs. Our results show that the RIMES statement reliably measures key dimensions of reporting quality. This tailored checklist is an important first step in improving the reporting quality of risk minimization evaluation studies and may ultimately help to improve the quality of these interventions themselves.
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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 | 0.398 | 0.493 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.017 | 0.008 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".