Analytical modelling techniques for enhancing tacrolimus dosing in solid organ transplantation: a systematic review protocol
Bibliographic record
Abstract
INTRODUCTION: Tacrolimus is an immunosuppressant commonly administered in transplant recipients. Given its narrow therapeutic range and susceptibility to various influencing variables, determining its optimal dosage is challenging. This systematic review seeks to identify effective analytical modelling techniques and methods for optimal tacrolimus dose prediction in solid transplant recipients. METHODS AND ANALYSIS: This review will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. The study will review the literature published from database inception to 11 March 2024, that assesses predictive models of tacrolimus dosing through analytical modelling techniques. We will include both randomised and non-randomised, as well as cross-sectional, qualitative and before-and-after studies and will perform our searches in four main databases-Ovid/MEDLINE, PubMed/MEDLINE, Scopus, Embase and Web of Science, and search engines including Centers for Disease Control (CDC) and Google Scholar. Papers that are not published in English or French are excluded from this study. A narrative synthesis and meta-analysis will be done if the extracted information permits such analysis. Conference abstracts will be ignored unless they are recent (published within 2 years of the search date). ETHICS AND DISSEMINATION: Ethics clearance is not required for this study as no primary data will be collected. The completed manuscript will be published, and the results of the study will be presented at conferences. STUDY REGISTRATION: International Prospective Register of Systematic Reviews (PROSPERO), CRD42024537212.
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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 | 0.070 | 0.080 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.014 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.079 | 0.011 |
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".