Evidence map›Paper›PMID 39191913›Full record

ArticleNPJ digital medicine2024

Personalized dose selection for the first Waldenström macroglobulinemia patient on the PRECISE CURATE.AI trial.

Agata Blasiak, Lester W J Tan, Li Ming Chong, Xavier Tadeo, Anh T L Truong, Kirthika Senthil Kumar, Yoann Sapanel, Michelle Poon, Raghav Sundar, Sanjay de Mel and 1 more

Abstract read
In one paragraph

Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Trial
  2. Review
  3. Article
  4. Article
  5. Personalized Medication for Chronic Diseases Using Multimodal Data-Driven Chain-of-Decisions.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Article
  6. Article
  7. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Agata BlasiakThe Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, 117456, Singapore. agatablasiak@gmail.com.ORCID http://orcid.org/0000-0003-0727-7611
Lester W J TanThe Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, 117456, Singapore.
Li Ming ChongThe Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, 117456, Singapore.ORCID http://orcid.org/0000-0002-3742-2704
Xavier TadeoThe Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, 117456, Singapore.ORCID http://orcid.org/0000-0003-0356-826X
Anh T L TruongThe Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, 117456, Singapore.
Kirthika Senthil KumarThe Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, 117456, Singapore.ORCID http://orcid.org/0000-0002-6412-5879
Yoann SapanelThe Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, 117456, Singapore.ORCID http://orcid.org/0000-0001-6797-7850
Michelle PoonDepartment of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore.
Raghav SundarThe N.1 Institute for Health (N.1), National University of Singapore, Singapore, 117456, Singapore.ORCID http://orcid.org/0000-0001-9423-1368
Sanjay de MelDepartment of Haematology-Oncology, National University Cancer Institute (NCIS), National University Hospital, Singapore, 119228, Singapore. sanjay_widanalage@nuhs.edu.sg.
Dean HoThe Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, 117456, Singapore. biedh@nus.edu.sg.ORCID http://orcid.org/0000-0002-7337-296X

Funding

MOH | National Medical Research Council (NMRC) MOH-OFLCG18May-0028National Research Foundation Singapore (National Research Foundation-Prime Minister's office, Republic of Singapore) AISG-GC-2019-002
6 · The paper itself

Abstract

The digital revolution in healthcare, amplified by the COVID-19 pandemic and artificial intelligence (AI) advances, has led to a surge in the development of digital technologies. However, integrating digital health solutions, especially AI-based ones, in rare diseases like Waldenström macroglobulinemia (WM) remains challenging due to limited data, among other factors. CURATE.AI, a clinical decision support system, offers an alternative to big data approaches by calibrating individual treatment profiles based on that individual's data alone. We present a case study from the PRECISE CURATE.AI trial with a WM patient, where, over two years, CURATE.AI provided dynamic Ibrutinib dose recommendations to clinicians (users) aimed at achieving optimal IgM levels. An 80-year-old male with newly diagnosed WM requiring treatment due to anemia was recruited to the trial for CURATE.AI-based dosing of the Bruton tyrosine kinase inhibitor Ibrutinib. The primary and secondary outcome measures were focused on scientific and logistical feasibility. Preliminary results underscore the platform's potential in enhancing user and patient engagement, in addition to clinical efficacy. Based on a two-year-long patient enrollment into the CURATE.AI-augmented treatment, this study showcases how AI-enabled tools can support the management of rare diseases, emphasizing the integration of AI to enhance personalized therapy.

Identifiers

PMID39191913
PMCPMC11350179

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.