ReviewThe AAPS journal2014
Dashboard systems: implementing pharmacometrics from bench to bedside.
Review in The AAPS journal, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of electronic audit and feedback: intervention effectiveness and use of behaviour change theory.Implementation science : IS · 2017Pooled it
- Determination of correlation of clearance with clinical outcomes for inflammatory bowel disease.World journal of gastroenterology · 2026Trial
- A model-based evaluation of single metrics for discriminating changes in rheumatoid arthritis disease activity.British journal of clinical pharmacology · 2016Trial
- Real-world clinical validation study: individualized warfarin dosing strategy integrating genetic polymorphisms and Bayesian.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Evaluation of a Therapeutic Drug Monitoring Strategy for Adalimumab in Psoriasis: A Prospective Pharmacokinetic-Pharmacodynamic Study.Clinical and translational science · 2026Observational
- The Coming of Age of Model-Informed Precision Therapeutics for Children.The journal of pediatric pharmacology and therapeutics : JPPT : the official journal of PPAG · 2025Article
- Exploring Health Care Professionals' Engagement With a Precision Dosing Calculator and Supporting Clinical Information: Insights From an Eye-Tracking and Usability Study.Therapeutic drug monitoring · 2025Article
- Model-Informed Precision Dosing of Infliximab in Korean Inflammatory Bowel Disease Patients: External Validation of Population Pharmacokinetic Models.CPT: pharmacometrics & systems pharmacology · 2025Article
- What's in a database? Insights from a retrospective review of penguin necropsy records in Aotearoa New Zealand.PloS one · 2025Article
- Enabling near real time use of wildlife necropsy data: Text-mining approaches to derive interactive dashboard displays.PloS one · 2025Article
- Personalized Secukinumab Treatment in Patients with Plaque Psoriasis Using Model-Informed Precision Dosing.Pharmaceutics · 2024Article
- Model-Informed Precision Dosing for Personalized Ustekinumab Treatment in Plaque Psoriasis.Pharmaceutics · 2024Article
- Acute severe ulcerative colitis: using JAK-STAT inhibitors for improved clinical outcomes.Frontiers in gastroenterology (Lausanne, Switzerland) · 2024Review
- Using Real-World Data to Guide Ustekinumab Dosing Strategies for Psoriasis: A Prospective Pharmacokinetic-Pharmacodynamic Study.Clinical and translational science · 2020Observational
- Improved Population Pharmacokinetic Model for Predicting Optimized Infliximab Exposure in Pediatric Inflammatory Bowel Disease.Inflammatory bowel diseases · 2020Article
- Asking More of Our EHR Systems to Improve Outcomes for Pediatric Patients.Frontiers in pharmacology · 2020Article
- Clinical Pharmacokinetic and Pharmacodynamic Considerations in the Treatment of Ulcerative Colitis.Clinical pharmacokinetics · 2019Review
- Innovative Study Designs Optimizing Clinical Pharmacology Research in Infants and Children.Journal of clinical pharmacology · 2018Review
- Call for action: how to improve use of patient-reported outcomes to guide clinical decision making in rheumatoid arthritis.Rheumatology international · 2018Review
- Personalized Drug Dosage - Closing the Loop.Pharmaceutical research · 2017Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, there has been increasing interest in the development of medical decision-support tools, including dashboard systems. Dashboard systems are software packages that integrate information and calculations about therapeutics from multiple components into a single interface for use in the clinical environment. Given the high cost of medical care, and the increasing need to demonstrate positive clinical outcomes for reimbursement, dashboard systems may become an important tool for improving patient outcome, improving clinical efficiency and containing healthcare costs. Similarly the costs associated with drug development are also rising. The use of model-based drug development (MBDD) has been proposed as a tool to streamline this process, facilitating the selection of appropriate doses and making informed go/no-go decisions. However, complete implementation of MBDD has not always been successful owing to a variety of factors, including the resources required to provide timely modeling and simulation updates. The application of dashboard systems in drug development reduces the resource requirement and may expedite updating models as new data are collected, allowing modeling results to be available in a timely fashion. In this paper, we present some background information on dashboard systems and propose the use of these systems both in the clinic and during drug development.
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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.