Evidence map›Paper›PMID 40321274›Full record

ArticlemedRxiv : the preprint server for health sciences2025

Construction of patient trajectories to model clinical trial outcomes: Application to Myasthenia Gravis.

Marc Garbey, Quentin Lesport, Henry J Kaminski

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Marc GarbeyCare Constitution Corp - Houston, TX, USA.ORCID 0000-0001-7898-9136
Quentin LesportCare Constitution Corp - Houston, TX, USA.ORCID 0009-0009-0039-7064
Henry J KaminskiDepartment of Neurology & Rehabilitation Medicine, George Washington University, Washington DC, USA.ORCID 0000-0002-8195-0141

Funding

Rare Disease Network for Myasthenia GravisU54NS115054 · NINDS · GEORGE WASHINGTON UNIVERSITY · PI HENRY J KAMINSKI · 2019 to 2026
$11.9M
NINDS NIH HHS U54 NS115054
6 · The paper itself

Abstract

Accurate prediction of patient outcomes in clinical trials is crucial for timely assessment of treatment efficacy. This study introduces a novel approach to predict patient response by constructing temporal trajectories from longitudinal clinical data. We aim to extrapolate these trajectories to forecast individual outcomes and identify when new patients align with established response patterns. Utilizing data from the MGTX trial of myasthenia gravis patients, we evaluate the predictability of these trajectories and discuss potential confounding factors. Furthermore, our analysis yields an automatic clustering of patients based on treatment success, revealing potential associations with age and smoking status.

Indexed as

clinical trialclustering techniquesdigital twindiplopiamuscular weaknessmyasthenia gravisneurology diseaseprecision medicineptosis

Identifiers

PMID40321274
PMCPMC12047874

What OpenQuestion holds

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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 OpenQuestion graph.