Global PRoMiSe (Perioperative Recommendations for Medication Safety): protocol for a mixed-methods study
Bibliographic record
Abstract
INTRODUCTION: Medication errors (MEs), which occur commonly in the perioperative period, have the potential to cause patient harm or death. Many published recommendations exist for preventing perioperative MEs; however, many of these recommendations conflict and are often not applicable to middle-income and low-income countries. The goal of this study is to develop and disseminate consensus-based recommendations for perioperative medication safety that are tailored to country income level. METHODS AND ANALYSIS: The primary site of this mixed-methods study is Massachusetts General Hospital/Harvard Medical School. Participants include a minimum of 108 international medication safety experts, 27 from each of the World Bank's four country income groups (high, upper-middle, lower-middle and low-income). Using the Delphi method, participants will rate the appropriateness of candidate medication safety recommendations by completing online surveys using RedCAP. We will use Condorcet ranking methods to prioritise the final recommendations for each country income group. We will execute a comprehensive dissemination strategy for the recommendations across each country income group. Finally, we will conduct semistructured interviews with our participants to evaluate the initial adoption and implementation of the recommendations in each country income group. ETHICS AND DISSEMINATION: This study was approved by the Human Research Committee/Institutional Review Board at Partners Healthcare (2019P003567). Findings will be published in peer-reviewed journals and presented at local and international conferences. TRIAL REGISTRATION NUMBER: NCT04240301.
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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 | 0.093 | 0.076 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.109 | 0.017 |
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".