The Impact of Brexit on the Pharmaceutical Supply Chain of the United Kingdom: Scoping Review Protocol
Bibliographic record
Abstract
BACKGROUND: The continuing uncertainty around Brexit has caused concern in the pharmaceutical industry and among health care professionals and patients. The exact consequences of Brexit on the pharmaceutical supply chain in the United Kingdom will depend on whether a deal is reached and what it entails, but it is likely to be affected by the withdrawal of the United Kingdom from the European Union. Regulatory issues and delays in supply have the potential to negatively affect the ability of UK residents to receive an adequate and timely supply of necessary medicines. OBJECTIVE: The purpose of this protocol is to provide an overview and critical analysis of current perspectives on the effect of Brexit on the UK pharmaceutical supply chain. METHODS: The PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) guidelines will be used to structure this protocol. A systematic search of MEDLINE, EMBASE, PsycINFO, Healthcare Management Information Consortium (HMIC), Cochrane, Web of Science, Business Source Complete, EconLit, and Economist Intelligence Unit will be conducted, as well as a Google and Nexis.UK search for grey literature such as reports, opinion pieces, and press releases. Two reviewers will independently screen the titles and abstracts of identified references and select studies according to the eligibility criteria. Any discrepancies will then be discussed and resolved. One reviewer will extract data from the included studies into a standardized form, which will be validated by a second reviewer. Risk of bias will be assessed using the Cochrane Collaboration Risk of Bias tool for any randomized controlled trials; quality will be assessed using the relevant Critical Appraisal Skills Programme (CASP) checklists; and grey literature will be assessed using the Authority, Accuracy, Coverage, Objectivity, Date, Significance (AACODS) checklist. Outcomes include the agreement between sources on the potential, likelihood, and severity of the consequences of Brexit on the UK pharmaceutical supply chain. RESULTS: Results will be included in the scoping review, which will be published in 2020. CONCLUSIONS: This scoping review will summarize the currently expected consequences of Brexit on the UK pharmaceutical supply chain. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/17684.
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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 | 0.096 | 0.107 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.014 | 0.014 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.090 | 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".