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1.

001-es BibID:BIBFORM126277
035-os BibID:(scopus)85201493485
Első szerző:De Brouwer, Edward
Cím:Machine-learning-based prediction of disability progression in multiple sclerosis : an observational, international, multi-center study / De Brouwer E., Becker T., Werthen-Brabants L., Dewulf P., Iliadis D., Dekeyser C., Laureys G., Van Wijmeersch B., Popescu V., Dhaene T., Deschrijver D., Waegeman W., De Baets B., Stock M., Horakova D., Patti F., Izquierdo G., Eichau S., Girard M., Prat A., Lugaresi A., Grammond P., Kalincik T., Alroughani R., Grand'Maison F., Skibina O., Terzi M., Lechner-Scott J., Gerlach O., Khoury S. J., Cartechini E., Van Pesch V., Sa M. J., Weinstock-Guttman B., Blanco Y., Ampapa R., Spitaleri D., Solaro C., Maimone D., Soysal A., Iuliano G., Gouider R., Castillo-Trivino T., Sánchez-Menoyo J. L., Laureys G., van der Walt A., Oh J., Aguera-Morales E., Altintas A., Al-Asmi A., de Gans K., Fragoso Y., Csepany T., Hodgkinson S., Deri N., Al-Harbi T., Taylor B., Gray O., Lalive P., Rozsa C., McGuigan C., Kermode A., Sempere A. P., Mihaela S., Simo M., Hardy T., Decoo D., Hughes S., Grigoriadis N., Sas A., Vella N., Moreau Y., Peeters L.
Dátum:2024
ISSN:2767-3170
Megjegyzések:Background Disability progression is a key milestone in the disease evolution of people with multiple sclerosis (PwMS). Prediction models of the probability of disability progression have not yet reached the level of trust needed to be adopted in the clinic. A common benchmark to assess model development in multiple sclerosis is also currently lacking. Methods Data of adult PwMS with a follow-up of at least three years from 146 MS centers, spread over 40 countries and collected by the MSBase consortium was used. With basic inclusion criteria for quality requirements, it represents a total of 15, 240 PwMS. External validation was performed and repeated five times to assess the significance of the results. Transparent Reporting for Individual Prognosis Or Diagnosis (TRIPOD) guidelines were followed. Confirmed disability progression after two years was predicted, with a confirmation window of six months. Only routinely collected variables were used such as the expanded disability status scale, treatment, relapse information, and MS course. To learn the probability of disability progression, state-of-the-art machine learning models were investigated. The discrimination performance of the models is evaluated with the area under the receiver operator curve (ROC-AUC) and under the precision recall curve (AUC-PR), and their calibration via the Brier score and the expected calibration error. All our preprocessing and model code are available at https://gitlab.com/edebrouwer/ms_benchmark, making this task an ideal benchmark for predicting disability progression in MS. Findings Machine learning models achieved a ROC-AUC of 0?71 ? 0?01, an AUC-PR of 0?26 ? 0?02, a Brier score of 0?1 ? 0?01 and an expected calibration error of 0?07 ? 0?04. The history of disability progression was identified as being more predictive for future disability progression than the treatment or relapses history. Conclusions Good discrimination and calibration performance on an external validation set is achieved, using only routinely collected variables. This suggests machine-learning models can reliably inform clinicians about the future occurrence of progression and are mature for a clinical impact study.
Tárgyszavak:Orvostudományok Klinikai orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Machine learning
disability progression
multiple sclerosis
Megjelenés:PLOS Digital Health. - 3 : 7 (2024), p. 1-25. -
További szerzők:Becker, Thijs Werthen-Brabants, Lorin Dewulf, Pieter Iliadis, Dimitrios Dekeyser, Cathérine Laureys, Guy Wijmeersch, Bart Van Popescu, Veronica Dhaene, Tom Deschrijver, Dirk Waegeman, Willem De Baets, Bernard Stock, Michael J. Horakova, Dana Patti, Francesco Izquierdo, Guillermo Eichau, Sara Girard, Marc Prat, Alexandre Lugaresi, Alessandra Grammond, Pierre Kalincik, Tomas Alroughani, Raed Grand'Maison, Francois Skibina, Olga Terzi, Murat Lechner-Scott, Jeannette Gerlach, Oliver Khoury, Samia J. Cartechini, Elisabetta Pesch, Vincent van Sá, Maria José Weinstock-Guttman, Bianca Blanco, Yolanda Ampapa, Radek Spitaleri, Daniele Solaro, Claudio Maimone, Davide Soysal, Aysun Iuliano, Gerardo Gouider, Riadh Castillo Triviño, Tamara Sanchez-Menoyo, Jose Laureys, Guy (Universitary Hospital Ghent) Walt, Anneke van der Oh, Jiwon Aguera-Morales, Eduardo Altintas, Ayse Al-Asmi, Abdullah de Gans, Koen Fragoso, Yara Csépány Tünde (1956-) (neurológus, pszichiáter) Hodgkinson, Suzanne Deri, Norma Al-Harbi, Talal Taylor, Bruce V. Gray, Orla Lalive, Patrice H. Rózsa Csilla McGuigan, Christopher Kermode, Allan G. Sempere, Perez A. Mihaela, Simu Simó Magdolna Hardy, Todd A. Decoo, Danny Hughes, Stella Grigoriadis, Nikolaos Sas Attila Vella Norbert Moreau, Yves Peeters, Liesbet
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2.

001-es BibID:BIBFORM103011
035-os BibID:(Scopus)85102090793 (Wos)000656637200025
Első szerző:Kalincik, Tomas
Cím:Effect of Disease-Modifying Therapy on Disability in Relapsing-Remitting Multiple Sclerosis Over 15 Years / Kalincik Tomas, Diouf Ibrahima, Sharmin Sifat, Malpas Charles, Spelman Tim, Horakova Dana, Havrdova Eva Kubala, Trojano Maria, Izquierdo Guillermo, Lugaresi Alessandra, Prat Alexandre, Girard Marc, Duquette Pierre, Grammond Pierre, Jokubaitis Vilija, van der Walt Anneke, Grand'Maison Francois, Sola Patrizia, Ferraro Diana, Shaygannejad Vahid, Alroughani Raed, Hupperts Raymond, Terzi Murat, Boz Cavit, Lechner-Scott Jeannette, Pucci Eugenio, Van Pesch Vincent, Granella Franco, Bergamaschi Roberto, Spitaleri Daniele, Slee Mark, Vucic Steve, Ampapa Radek, McCombe Pamela, Ramo-Tello Cristina, Prevost Julie, Olascoaga Javier, Cristiano Edgardo, Barnett Michael, Saladino Maria Laura, Sanchez-Menoyo Jose Luis, Hodgkinson Suzanne, Rozsa Csilla, Hughes Stella, Moore Fraser, Shaw Cameron, Butler Ernest, Skibina Olga, Gray Orla, Kermode Allan, Csepany Tunde, Singhal Bhim, Shuey Neil, Piroska Imre, Taylor Bruce, Simo Magdolna, Sirbu Carmen-Adella, Sas Attila, Butzkueven Helmut, MSBase Study Group
Dátum:2021
ISSN:0028-3878 1526-632X
Megjegyzések:Objective To test the hypothesis that immunotherapy prevents long-term disability in relapsing-remitting multiple sclerosis (MS), we modeled disability outcomes in 14,717 patients. Methods We studied patients from MSBase followed for ?1 year, with ?3 visits, ?1 visit per year, and exposed to MS therapy, and a subset of patients with ?15-year follow-up. Marginal structural models were used to compare the cumulative hazards of 12-month confirmed increase and decrease in disability, Expanded Disability Status Scale (EDSS) step 6, and the incidence of relapses between treated and untreated periods. Marginal structural models were continuously readjusted for patient age, sex, pregnancy, date, disease course, time from first symptom, prior relapse history, disability, and MRI activity. Results A total of 14,717 patients were studied. During the treated periods, patients were less likely to experience relapses (hazard ratio 0.60, 95% confidence interval [CI] 0.43?0.82, p = 0.0016), worsening of disability (0.56, 0.38?0.82, p = 0.0026), and progress to EDSS step 6 (0.33, 0.19?0.59, p = 0.00019). Among 1,085 patients with ?15-year follow-up, the treated patients were less likely to experience relapses (0.59, 0.50?0.70, p = 10?9) and worsening of disability (0.81, 0.67?0.99, p = 0.043). Conclusion Continued treatment with MS immunotherapies reduces disability accrual by 19%?44% (95% CI 1%?62%), the risk of need of a walking aid by 67% (95% CI 41%?81%), and the frequency of relapses by 40?41% (95% CI 18%?57%) over 15 years. This study provides evidence that disease-modifying therapies are effective in improving disability outcomes in relapsing-remitting MS over the long term. Classification of Evidence This study provides Class IV evidence that, for patients with relapsing-remitting MS, long-term exposure to immunotherapy prevents neurologic disability.
Tárgyszavak:Orvostudományok Klinikai orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:Neurology. - 96 : 5 (2021), p. e783-e797. -
További szerzők:Diouf, Ibrahima Sharmin, Sifat Malpas, Charles Spelman, Tim Horakova, Dana Havrdova, Eva Trojano, Maria Izquierdo, Guillermo Lugaresi, Alessandra Prat, Alexandre Girard, Marc Duquette, Pierre Grammond, Pierre Jokubaitis, Vilija Walt, Anneke van der Grand'Maison, Francois Sola, Patrizia Ferraro, Diana Shaygannejad, Vahid Alroughani, Raed Hupperts, Raymond Terzi, Murat Boz, Cavit Lechner-Scott, Jeannette Pucci, Eugenio Pesch, Vincent van Granella, Franco Bergamaschi, Roberto Spitaleri, Daniele Slee, Mark Vucic, Steve Ampapa, Radek McCombe, Pamela Ramo-Tello, Cristina Prevost, Julie Olascoaga, Javier Cristiano, Edgardo Barnett, Michael Saladino, Maria Laura Sanchez-Menoyo, Jose Hodgkinson, Suzanne Rózsa Csilla Hughes, Stella Moore, Fraser Shaw, Cameron Butler, Ernest Skibina, Olga Gray, Orla Kermode, Allan G. Csépány Tünde (1956-) (neurológus, pszichiáter) Singhal, Bhim Shuey, Neil Piroska Imre Taylor, Bruce V. Simó Magdolna Sirbu, Carmen-Adella Sas Attila Butzkueven, Helmut MSBase Study Group
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3.

001-es BibID:BIBFORM083272
Első szerző:Kalincik, Tomas
Cím:Immunotherapy prevents long-term disability in relapsing multiple sclerosis over 15 years / Tomas Kalincik, Sifat Sharmin, Charles Malpas, Tim Spelman, Dana Horakova, Eva Kubala Havrdova, Maria Trojano, Guillermo Izquierdo, Alessandra Lugaresi, Alexandre Prat, Marc Girard, Pierre Duquette, Pierre Grammond, Vilija Jokubaitis, Anneke van der Walt, Francois Grand'Maison, Patrizia Sola, Diana Ferraro, Vahid Shaygannejad, Raed Alroughani, Raymond Hupperts, Murat Terzi, Cavit Boz, Jeannette Lechner-Scott, Eugenio Pucci, Vincent Van Pesch, Franco Granella, Roberto Bergamaschi, Daniele Spitaleri, Mark Slee, Steve Vucic, Radek Ampapa, Pamela McCombe, Cristina Ramo-Tello, Julie Prevost, Javier Olascoaga, Edgardo Cristiano, Michael Barnett, Maria Laura Saladino, Jose Luis Sanchez-Menoyo, Suzanne Hodgkinson, Csilla Rozsa, Stella Hughes, Fraser Moore, Cameron Shaw, Ernest Butler, Olga Skibina, Orla Gray, Allan Kermode, Tunde Csepany, Bhim Singhal, Neil Shuey, Imre Piroska, Bruce Taylor, Magdolna Simo, Carmen-Adella Sirbu, Attila Sas, Helmut Butzkueven, MSBase Study group
Dátum:2019
Tárgyszavak:Orvostudományok Klinikai orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:bioRxiv. - 2019 (2019), p. 1-41. -
További szerzők:Sharmin, Sifat Malpas, Charles Spelman, Tim Horakova, Dana Havrdova, Eva Trojano, Maria Izquierdo, Guillermo Lugaresi, Alessandra Prat, Alexandre Girard, Marc Duquette, Pierre Grammond, Pierre Jokubaitis, Vilija Walt, Anneke van der Grand'Maison, Francois Sola, Patrizia Ferraro, Diana Shaygannejad, Vahid Alroughani, Raed Hupperts, Raymond Terzi, Murat Boz, Cavit Lechner-Scott, Jeannette Pucci, Eugenio Pesch, Vincent van Granella, Franco Bergamaschi, Roberto Spitaleri, Daniele Slee, Mark Vucic, Steve Ampapa, Radek McCombe, Pamela Ramo-Tello, Cristina Prevost, Julie Olascoaga, Javier Cristiano, Edgardo Barnett, Michael Saladino, Maria Laura Sanchez-Menoyo, Jose Hodgkinson, Suzanne Rózsa Csilla Hughes, Stella Moore, Fraser Shaw, Cameron Butler, Ernest Skibina, Olga Gray, Orla Kermode, Allan G. Csépány Tünde (1956-) (neurológus, pszichiáter) Singhal, Bhim Shuey, Neil Piroska Imre Taylor, Bruce V. Simó Magdolna Sirbu, Carmen-Adella Sas Attila Butzkueven, Helmut MSBase Study Group
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4.

001-es BibID:BIBFORM132947
035-os BibID:(scopus)105011413182 (wos)001536298500003
Első szerző:Pirmani, Ashkan
Cím:Personalized federated learning for predicting disability progression in multiple sclerosis using real-world routine clinical data / Pirmani Ashkan, De Brouwer Edward, Arany Ádám, Oldenhof Martijn, Passemiers Antoine, Faes Axel, Kalincik Tomas, Ozakbas Serkan, Gouider Riadh, Willekens Barbara, Horakova Dana, Havrdova Eva Kubala, Patti Francesco, Prat Alexandre, Lugaresi Alessandra, Tomassini Valentina, Grammond Pierre, Cartechini Elisabetta, Roos Izanne, Boz Cavit, Alroughani Raed, Amato Maria Pia, Buzzard Katherine, Lechner-Scott Jeannette, Guimaraes Joana, Solaro Claudio, Gerlach Oliver, Soysal Aysun, Kuhle Jens, Sanchez-Menoyo Jose Luis, Spitaleri Daniele, Csepany Tunde, Van Wijmeersch Bart, Ampapa Radek, Prevost Julie, Khoury Samia J., Van Pesch Vincent, John Nevin, Maimone Davide, Weinstock-Guttman Bianca, Laureys Guy, McCombe Pamela, Blanco Yolanda, Altintas Ayse, Al-Asmi Abdullah, Garber Justin, Van der Walt Anneke, Butzkueven Helmut, de Gans Koen, Rozsa Csilla, Taylor Bruce, Al-Harbi Talal, Sas Attila, Rajda Cecilia, Gray Orla, Decoo Danny, Carroll William M., Kermode Allan G., Fabis-Pedrini Marzena, Mason Deborah, Perez-Sempere Angel, Simu Mihaela, Shuey Neil, Singhal Bhim, Cauchi Marija, Hardy Todd A., Ramanathan Sudarshini, Lalive Patrice, Sirbu Carmen-Adella, Hughes Stella, Castillo Trivino Tamara, Peeters Liesbet M., Moreau Yves
Dátum:2025
ISSN:2398-6352
Megjegyzések:Early prediction of disability progression in multiple sclerosis (MS) remains challenging despite its critical importance for therapeutic decision-making. We present the first systematic evaluation of personalized federated learning (PFL) for 2-year MS disability progression prediction, leveraging multi-center real-world data from over 26,000 patients. While conventional federated learning (FL) enables privacy-aware collaborative modeling, it remains vulnerable to institutional data heterogeneity. PFL overcomes this challenge by adapting shared models to local data distributions without compromising privacy. We evaluated two personalization strategies: a novel AdaptiveDualBranchNet architecture with selective parameter sharing, and personalized fine-tuning of global models, benchmarked against centralized and client-specific approaches. Baseline FL underperformed relative to personalized methods, whereas personalization significantly improved performance, with personalized FedProx and FedAVG achieving ROC-AUC scores of 0.8398 ? 0.0019 and 0.8384 ? 0.0014, respectively. These findings establish personalization as critical for scalable, privacy-aware clinical prediction models and highlight its potential to inform earlier intervention strategies in MS and beyond.
Tárgyszavak:Orvostudományok Klinikai orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:npj Digital Medicine. - 8 : 1 (2025), p. 1-15. -
További szerzők:De Brouwer, Edward Arany Ádám Oldenhof, Martijn Passemiers, Antoine Faes, Axel Kalincik, Tomas Ozakbas, Serkan Gouider, Riadh Willekens, Barbara Horakova, Dana Havrdova, Eva Patti, Francesco Prat, Alexandre Lugaresi, Alessandra Tomassini, Valentina Grammond, Pierre Cartechini, Elisabetta Roos, Izanne Boz, Cavit Alroughani, Raed Amato, Maria Pia Buzzard, Katherine Lechner-Scott, Jeannette Guimaraes, Joana Solaro, Claudio Gerlach, Oliver Soysal, Aysun Kuhle, Jens Sanchez-Menoyo, Jose Spitaleri, Daniele Csépány Tünde (1956-) (neurológus, pszichiáter) Wijmeersch, Bart Van Ampapa, Radek Prevost, Julie Khoury, Samia J. Pesch, Vincent van John, Nevin Maimone, Davide Weinstock-Guttman, Bianca Laureys, Guy (Universitary Hospital Ghent) McCombe, Pamela Blanco, Yolanda Altintas, Ayse Al-Asmi, Abdullah Garber, Justin Walt, Anneke van der Butzkueven, Helmut de Gans, Koen Rózsa Csilla Taylor, Bruce V. Al-Harbi, Talal Sas Attila Rajda Cecília Gray, Orla Decoo, Danny Carroll, William M. Kermode, Allan G. Fabis-Pedrini, Marzena Mason, Deborah Perez-Sempere, Angel Simu, Mihaela Shuey, Neil Singhal, Bhim Cauchi, Marija Hardy, Todd A. Ramanathan, Sudarshini Lalive, Patrice H. Sirbu, Carmen-Adella Hughes, Stella Castillo Triviño, Tamara Peeters, Liesbet Moreau, Yves
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5.

001-es BibID:BIBFORM119143
035-os BibID:(scopus)85181176590 (wos)001130397900001
Első szerző:Spelman, Tim
Cím:Comparative effectiveness and cost-effectiveness of natalizumab and fingolimod in rapidly evolving severe relapsing-remitting multiple sclerosis in the United Kingdom / Spelman T., Herring W. L., Acosta C., Hyde R., Jokubaitis V. G., Pucci E., Lugaresi A., Laureys G., Havrdova E. K., Horakova D., Izquierdo G., Eichau S., Ozakbas S., Alroughani R., Kalincik T., Duquette P., Girard M., Petersen T., Patti F., Csepany T., Granella F., Grand'Maison F., Ferraro D., Karabudak R., Jose Sa M., Trojano M., van Pesch V., Van Wijmeersch B., Cartechini E., McCombe P., Gerlach O., Spitaleri D., Rozsa C., Hodgkinson S., Bergamaschi R., Gouider R., Soysal A., Prevost J., Garber J., de Gans K., Ampapa R., Simo M., Sanchez-Menoyo J. L., Iuliano G., Sas A., van der Walt A., John N., Gray O., Hughes S., De Luca G., Onofrj M., Buzzard K., Skibina O., Terzi M., Slee M., Solaro C., Ramo-Tello C., Fragoso Y., Shaygannejad V., Moore F., Rajda C., Aguera-Morales E., Butzkueven H.
Dátum:2024
ISSN:1369-6998 1941-837X
Megjegyzések:Aim To evaluate the real-world comparative effectiveness and the cost-effectiveness, from a UK National Health Service perspective, of natalizumab versus fingolimod in patients with rapidly evolving severe relapsing-remitting multiple sclerosis (RES-RRMS). Methods Real-world data from the MSBase Registry were obtained for patients with RES-RRMS who were previously either naive to disease-modifying therapies or had been treated with interferon-based therapies, glatiramer acetate, dimethyl fumarate, or teriflunomide (collectively known as BRACETD). Matched cohorts were selected by 3-way multinomial propensity score matching, and the annualized relapse rate (ARR) and 6-month?confirmed disability worsening (CDW6M) and improvement (CDI6M) were compared between treatment groups. Comparative effectiveness results were used in a cost-effectiveness model comparing natalizumab and fingolimod, using an established Markov structure over a lifetime horizon with health states based on the Expanded Disability Status Scale. Additional model data sources included the UK MS Survey 2015, published literature, and publicly available sources. Results In the comparative effectiveness analysis, we found a significantly lower ARR for patients starting natalizumab compared with fingolimod (rate ratio [RR]?=?0.65; 95% confidence interval [CI], 0.57?0.73) or BRACETD (RR = 0.46; 95% CI, 0.42?0.53). Similarly, CDI6M was higher for patients starting natalizumab compared with fingolimod (hazard ratio [HR]?=?1.25; 95% CI, 1.01?1.55) and BRACETD (HR = 1.46; 95% CI, 1.16?1.85). In patients starting fingolimod, we found a lower ARR (RR = 0.72; 95% CI, 0.65?0.80) compared with starting BRACETD, but no difference in CDI6M (HR = 1.17; 95% CI, 0.91?1.50). Differences in CDW6M were not found between the treatment groups. In the base-case cost-effectiveness analysis, natalizumab dominated fingolimod (0.302 higher quality-adjusted life-years [QALYs] and ?17,141 lower predicted lifetime costs). Similar cost-effectiveness results were observed across sensitivity analyses. Conclusions This MSBase Registry analysis suggests that natalizumab improves clinical outcomes when compared with fingolimod, which translates to higher QALYs and lower costs in UK patients with RES-RRMS.
Tárgyszavak:Orvostudományok Elméleti orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Multiple sclerosis
natalizumab
fingolimod
real-world data
comparative effectiveness
cost-effectiveness
Megjelenés:Journal of Medical Economics. - 27 : 1 (2024), p. 109-125. -
További szerzők:Herring, W. L. Acosta, C. Hyde, R. Jokubaitis, Vilija Pucci, Eugenio Lugaresi, Alessandra Laureys, Guy Havrdova, Eva Horakova, Dana Izquierdo, Guillermo Eichau, Sara Ozakbas, Serkan Alroughani, Raed Kalincik, Tomas Duquette, Pierre Girard, Marc Petersen, Thor Patti, Francesco Csépány Tünde (1956-) (neurológus, pszichiáter) Granella, Franco Grand'Maison, Francois Ferraro, D. Karabudak, Rana José Sá, Maria Trojano, Maria Pesch, Vincent van Wijmeersch, Bart Van Cartechini, Elisabetta McCombe, Pamela Gerlach, Oliver Spitaleri, Daniele Rózsa Csilla Hodgkinson, Suzanne Bergamaschi, Roberto Gouider, Riadh Soysal, Aysun Prevost, Julie Garber, Justin de Gans, Koen Ampapa, Radek Simó Magdolna Sanchez-Menoyo, Jose Iuliano, Gerardo Sas Attila Walt, Anneke van der John, Nevin Gray, Orla Hughes, Stella De Luca, Giacomo Onofrj, Marco Buzzard, Katherine Skibina, Olga Terzi, Murat Slee, Mark Solaro, Claudio Ramo-Tello, Cristina Fragoso, Yara Shaygannejad, Vahid Moore, Fraser Rajda Cecília Aguera-Morales, Eduardo Butzkueven, Helmut
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6.

001-es BibID:BIBFORM083268
Első szerző:Zhou, Yuan
Cím:Redefining the Multiple Sclerosis Severity Score (MSSS) : the effect of sex and onset phenotype / Yuan Zhou, Suzi B. Claflin, Jim Stankovich, Ingrid van der Mei, Steve Simpson, Richard H. Roxburgh, Tomas Kalincik, Leigh Blizzard, Alessandra Lugaresi, Raed Alroughani, Seyed Aidin Sajedi, Helmut Butzkueven, Eugenio Pucci, Daniele L. A. Spitaleri, Franco Granella, Edgardo Cristiano, Bassem Yamout, Stella Hughes, Riadh Gouider, José Luis Sánchez Menoyo, Javier Olascoaga, Chris McGuigan, Cameron Shaw, Allan G. Kermode, Krisztian Kasa, Talal Al-Harbi, Ayse Altintas, Guy Laureys, Yara Fragoso, Todd A. Hardy, Tunde Csepany, Carmen-Adella Sirbu, Danny Decoo, Attila Sas, Jose C. Alvarez-Cermeño, Karim Kotkata, Jorge Millán-Pascual, Bruce V. Taylor
Dátum:2020
ISSN:1352-4585
Tárgyszavak:Orvostudományok Klinikai orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:Multiple Sclerosis. - 26 : 13 (2020), p. 1765-1774. -
További szerzők:Claflin, Suzi B. Stankovich, Jim Mei, Ingrid van der Simpson, Steve Roxburgh, Richard H. Kalincik, Tomas Blizzard, Leigh Lugaresi, Alessandra Alroughani, Raed Sajedi, Seyed Aidin Butzkueven, Helmut Pucci, Eugenio Spitaleri, Daniele L. A. Granella, Franco Cristiano, Edgardo Yamout, Bassem Hughes, Stella Gouider, Riadh Sánchez Menoyo, José Luis Olascoaga, Javier McGuigan, Christopher Shaw, Cameron Kermode, Allan G. Kása Krisztián Al-Harbi, Talal Altintas, Ayse Laureys, Guy Fragoso, Yara Hardy, Todd A. Csépány Tünde (1956-) (neurológus, pszichiáter) Sirbu, Carmen-Adella Decoo, Danny Sas Attila Alvarez-Cermeño, Jose C. Kotkata, Karim Millán-Pascual, Jorge Taylor, Bruce V.
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