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Determinants of therapeutic lag in multiple sclerosis

Overview of attention for article published in Multiple Sclerosis (13524585), January 2021
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  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

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2 news outlets
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1 blog
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8 tweeters

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4 Mendeley
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Title
Determinants of therapeutic lag in multiple sclerosis
Published in
Multiple Sclerosis (13524585), January 2021
DOI 10.1177/1352458520981300
Pubmed ID
Authors

Izanne Roos, Emmanuelle Leray, Federico Frascoli, Romain Casey, J William L Brown, Dana Horakova, Eva Kubala Havrdova, Marc Debouverie, Maria Trojano, Francesco Patti, Guillermo Izquierdo, Sara Eichau, Gilles Edan, Alexandre Prat, Marc Girard, Pierre Duquette, Marco Onofrj, Alessandra Lugaresi, Pierre Grammond, Jonathan Ciron, Aurélie Ruet, Serkan Ozakbas, Jérôme De Seze, Céline Louapre, Hélène Zephir, Maria José Sá, Patrizia Sola, Diana Ferraro, Pierre Labauge, Gilles Defer, Roberto Bergamaschi, Christine Lebrun-Frenay, Cavit Boz, Elisabetta Cartechini, Thibault Moreau, David Laplaud, Jeannette Lechner-Scott, Francois Grand’Maison, Oliver Gerlach, Murat Terzi, Franco Granella, Raed Alroughani, Gerardo Iuliano, Vincent Van Pesch, Bart Van Wijmeersch, Daniele LA Spitaleri, Aysun Soysal, Eric Berger, Julie Prevost, Eduardo Aguera-Morales, Pamela McCombe, Tamara Castillo Triviño, Pierre Clavelou, Jean Pelletier, Recai Turkoglu, Bruno Stankoff, Olivier Gout, Eric Thouvenot, Olivier Heinzlef, Youssef Sidhom, Riadh Gouider, Tunde Csepany, Bertrand Bourre, Abdullatif Al Khedr, Olivier Casez, Philippe Cabre, Alexis Montcuquet, Abir Wahab, Jean-Philippe Camdessanche, Aude Maurousset, Ivania Patry, Karolina Hankiewicz, Corinne Pottier, Nicolas Maubeuge, Céline Labeyrie, Chantal Nifle, Alasdair Coles, Charles B Malpas, Sandra Vukusic, Helmut Butzkueven, Tomas Kalincik

Abstract

A delayed onset of treatment effect, termed therapeutic lag, may influence the assessment of treatment response in some patient subgroups. The objective of this study is to explore the associations of patient and disease characteristics with therapeutic lag on relapses and disability accumulation. Data from MSBase, a multinational multiple sclerosis (MS) registry, and OFSEP, the French MS registry, were used. Patients diagnosed with MS, minimum 1 year of exposure to MS treatment and 3 years of pre-treatment follow-up, were included in the analysis. Studied outcomes were incidence of relapses and disability accumulation. Therapeutic lag was calculated using an objective, validated method in subgroups stratified by patient and disease characteristics. Therapeutic lag under specific circumstances was then estimated in subgroups defined by combinations of clinical and demographic determinants. High baseline disability scores, annualised relapse rate (ARR) ⩾ 1 and male sex were associated with longer therapeutic lag on disability progression in sufficiently populated groups: females with expanded disability status scale (EDSS) < 6 and ARR < 1 had mean lag of 26.6 weeks (95% CI = 18.2-34.9), males with EDSS < 6 and ARR < 1 31.0 weeks (95% CI = 25.3-36.8), females with EDSS < 6 and ARR ⩾ 1 44.8 weeks (95% CI = 24.5-65.1), and females with EDSS ⩾ 6 and ARR < 1 54.3 weeks (95% CI = 47.2-61.5). Pre-treatment EDSS and ARR are the most important determinants of therapeutic lag.

Twitter Demographics

The data shown below were collected from the profiles of 8 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 50%
Professor > Associate Professor 1 25%
Unknown 1 25%
Readers by discipline Count As %
Immunology and Microbiology 1 25%
Social Sciences 1 25%
Neuroscience 1 25%
Medicine and Dentistry 1 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 29. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 March 2021.
All research outputs
#844,470
of 17,367,552 outputs
Outputs from Multiple Sclerosis (13524585)
#108
of 3,044 outputs
Outputs of similar age
#31,081
of 392,205 outputs
Outputs of similar age from Multiple Sclerosis (13524585)
#6
of 79 outputs
Altmetric has tracked 17,367,552 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,044 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.8. This one has done particularly well, scoring higher than 96% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 392,205 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 79 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 93% of its contemporaries.