Impact of supply chain disruptions and drug shortages on drug utilization: A scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: This proposed scoping review aims to examine studies assessing the impact of drug shortages on population-level drug utilization trends. The objectives of this review are to a) assess which drugs have been studied and describe associated drug characteristics, b) determine jurisdictions and healthcare settings that have conducted these studies, and c) describe how changes in drug use and the extent of shortage impacts are reported in literature. INTRODUCTION: Drug shortages continue to impair drug access and delivery of quality care across the world. However, the impact of drug supply disruptions on availability and drug use are understudied in current literature. This proposed scoping review will identify this gap and inform future research initiatives aimed at determining the real-world impacts of drug shortages. INCLUSION CRITERIA: Published and unpublished observational studies reporting on the effects of drug supply chain disruptions (shortages, discontinuations, and safety-based withdrawals) on consequent utilization trends faced by pharmaceutical products (i.e. prescription drugs, over-the-counter drugs, vaccines, therapy products, pharmaceutical solutions). Literature reviews, meta-analyses, randomized control trials, case series, case reports, and opinion pieces will be excluded. METHODS: The search strategy will combine two key search concepts: drug shortages and drug utilization. The search will be conducted in MEDLINE and EMBASE. This will be followed by an extensive grey literature search in grey literature databases, targeted websites and Google. Furthermore, reference lists of included articles will be searched. Articles will be independently screened, selected and extracted by two reviewers. Data will be descriptively analyzed and presented in tables. TRIAL REGISTRATION: Review registration number: Open Science Framework, https://osf.io/2p6e5.
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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 | 0.106 | 0.082 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.024 | 0.016 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.009 | 0.004 |
| Insufficient payload (model declined to judge) | 0.047 | 0.012 |
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".