ArticlePharmacogenomics and personalized medicine2022
Prediction of Tacrolimus Dose/Weight-Adjusted Trough Concentration in Pediatric Refractory Nephrotic Syndrome: A Machine Learning Approach.
Article in Pharmacogenomics and personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 7 citations in OpenAlex.
- Rising Role of Artificial Intelligence in Clinical Pharmacometrics and Model-Informed Precision Dosing in Pediatrics.The journal of pediatric pharmacology and therapeutics : JPPT : the official journal of PPAG · 2026Article
- Predicting the blood concentration of levetiracetam in people with epilepsy using machine learning and real-world data.Therapeutic advances in psychopharmacology · 2026Article
- Integration of urine retinol-binding protein and genetic markers for early prediction of tacrolimus nephrotoxicity using machine learning.Translational pediatrics · 2025Article
- Optimizing tacrolimus dosage in post-renal transplantation using DoseOptimal framework: profiling CYP3A5 genetic variants for interpretability.International journal of clinical pharmacy · 2025Article
- Prediction of High-Dose Methotrexate Blood Concentration in Osteosarcoma Patients Using Machine Learning.Drug design, development and therapy · 2025Article
- Clinical-oriented tacrolimus dosing algorithms in kidney transplant based on genetic algorithm and deep forest.Frontiers in pharmacology · 2025Article
- Machine learning-based prediction model for teicoplanin plasma concentrations in adults with liver disease using real-world data.Frontiers in pharmacology · 2025Article
- Steroid-Induced Psychosis in a Child With Nephrotic Syndrome Secondary to Focal Segmental Glomerulosclerosis: A Case Report.Clinical case reports · 2024Article
- Development and evaluation of uncertainty quantifying machine learning models to predict piperacillin plasma concentrations in critically ill patients.BMC medical informatics and decision making · 2022Article
- Tacrolimus pharmacokinetics in pediatric nephrotic syndrome: A combination of population pharmacokinetic modelling and machine learning approaches to improve individual prediction.Frontiers in pharmacology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTacrolimus (TAC) is a first-line immunosuppressant for patients with refractory nephrotic syndrome (NS). However, there is a high inter-patient variability of TAC pharmacokinetics, thus therapeutic drug monitoring (TDM) is required. In this study, we aimed to employ machine learning algorithms to investigate the impact of clinical and genetic variables on the TAC dose/weight-adjusted trough concentration (C PATIENTS AND
methodsThe association of 82 clinical variables and 244 single nucleotide polymorphisms (SNPs) with TAC C
resultsGBDT algorithm performed best in the whole group (R
conclusionThe association between the clinical and genetic variables and TAC C
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.