Evidence map›Paper›PMID 41189285›Full record

ArticleCPT: pharmacometrics & systems pharmacology2025

Predictors of Ability to Work in Multiple Sclerosis.

Gustaf J Wellhagen, Sebastian Ueckert, Elisabet Nielsen, Carl Smith, Xinyi Li, Joachim Burman, Mats O Karlsson

Abstract read
In one paragraph

Article in CPT: pharmacometrics & systems pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–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

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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

7 authors.

Gustaf J WellhagenDepartment of Pharmacy, Uppsala University, Uppsala, Sweden.ORCID 0000-0002-7228-0422
Sebastian UeckertDepartment of Pharmacy, Uppsala University, Uppsala, Sweden.ORCID 0000-0002-3712-0255
Elisabet NielsenDepartment of Pharmacy, Uppsala University, Uppsala, Sweden.ORCID 0000-0003-0725-214X
Carl SmithDepartment of Pharmacy, Uppsala University, Uppsala, Sweden.
Xinyi LiDepartment of Pharmacy, Uppsala University, Uppsala, Sweden.
Joachim BurmanDepartment of Medical Sciences, Uppsala University, Uppsala, Sweden.ORCID 0000-0002-7045-1806
Mats O KarlssonDepartment of Pharmacy, Uppsala University, Uppsala, Sweden.ORCID 0000-0003-1258-8297

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Multiple sclerosis (MS) is a chronic disorder that typically shows accumulation of disability, affecting the ability to work. The disease severity is usually graded by physicians with the expanded disability status scale (EDSS) in the clinic, but patient-reported outcome questionnaires are also available, like the Multiple Sclerosis Impact Scale (MSIS-29) or Fatigue Scale for Motor and Cognitive Functions (FSMC). The aim of this work was to investigate the quantitative link between disease severity and the number of days with MS-related sickness benefits from registry data (the Swedish MS registry and the Swedish Social Insurance Agency's Micro Data for Analyzes of Social Insurance registry). An item response theory model for the disability was built, linking the EDSS, MSIS-29, and FSMC to the same underlying disease construct through five correlated latent variables. A Markov state model for the level of sickness benefits was also developed, in which the disease severities from the disability model were tested as covariates, on top of age. The latent variable for EDSS was the most important predictor of work ability. Patients with low disability (EDSS < 3) hardly had any sickness benefit days, while patients with severe disability (EDSS ≥ 6) were found to spend over 50% of their time with sickness benefits. Physical aspects of the disease were found to be more important than psychological aspects in predicting work ability. This underlines the patient-specific nature of MS, and the need for predictive models such as these to evaluate treatment effects, make risk assessments, and calculate societal and individual costs.

Indexed as

Multiple SclerosisAdultDisability EvaluationFemaleHumansMaleMarkov ChainsMiddle AgedPersons with DisabilitiesRegistriesSeverity of Illness IndexSick LeaveSurveys and QuestionnairesSwedenitem response theorymultiple sclerosismultistate Markov modelpatient‐reported outcomesPharmacometrics

Identifiers

PMID41189285
PMCPMC12706428

What OpenQuestion holds

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LicenceCC BY-NC
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 OpenQuestion graph.