Strategies for measuring prescription medication switching with pharmacy claims data: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: This scoping review will aim to compare strategies for measuring prescription medication switching with pharmacy claims data, with a focus on psychotropic vs non-psychotropic medications. INTRODUCTION: Medication switching (ie, the replacement of one medication for another) is common and occurs due to several factors (such as adverse effects to a specific medication). In pharmacoepidemiology studies that use pharmacy claims data, it is important to identify and account for switches; however, due to data limitations and lack of a methodological standard, this can be challenging. The aim of this scoping review is to describe how studies have previously measured medication switching with pharmacy claims data in order to create a repository of common strategies and highlight areas for future research. INCLUSION CRITERIA: This review will include studies that have used pharmacy claims data to measure medication switching as their primary independent or dependent variable. Studies conducted at the individual level (ie, not ecological), published between January 1, 1980, and October 31, 2020, and investigating orally administered, non-anti-infective medications will be considered. No age, population, or context restrictions are specified as inclusion criteria. METHODS: JBI methodology and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews were used for this protocol. MEDLINE (PubMed), Embase (Ovid), Central (Cochrane Library), CINAHL (EBSCO), and Google Scholar will be searched with the assistance of a health sciences research librarian. Two reviewers will independently screen titles, abstracts, and full-text articles. Strategies for measuring medication switching will be narratively described and summarized overall and by broad medication class.
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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 | 0.151 | 0.129 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.014 | 0.018 |
| Bibliometrics | 0.029 | 0.022 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.071 | 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".