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51. Updated Results of the COVID-19 in MS Global Data Sharing Initiative Anti-CD20 and Other Risk Factors Associated With COVID-19 Severity

52. Impact of methodological choices in comparative effectiveness studies: application in natalizumab versus fingolimod comparison among patients with multiple sclerosis

53. Disease Reactivation After Cessation of Disease-Modifying Therapy in Patients With Relapsing-Remitting Multiple Sclerosis

54. Multiple Sclerosis Severity Score (MSSS) improves the accuracy of individualized prediction in MS

55. Comparative effectiveness of cladribine tablets versus other oral disease-modifying treatments for multiple sclerosis: results from MSBase registry

56. Impact of methodological choices in comparative effectiveness studies:application in natalizumab versus fingolimod comparison among patients with multiple sclerosis

57. Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression (vol 208, 106180, 2021)

58. Early predictors of disability in paediatric multiple sclerosis: evidence from a multi-national cohort

61. The 20-year history: Change of multiple sclerosis patient profile over 20 years

62. The outcome of a national MS-Covid-19 study: What the Turkish MS cohort reveals?

63. Real-world experience with ocrelizumab in the msbase registry.

64. Variability of the response to immunotherapy among subgroups of patients with multiple sclerosis.

65. Comparison of the effectiveness of ocrelizumab vs interferons, fingolimod and natalizumab on relapses in relapsing-remitting multiple sclerosis.

66. Variability of the Response to Immunotherapy among Sub-groups of Patients with Multiple Sclerosis.

67. Association of latitude and exposure to ultraviolet B radiation with severity of multiple sclerosis.

68. Disability accrual in primary-progressive & secondaryprogressive multiple sclerosis.

69. Relapse during the washout period predicts time to relapse after switching to cladribine.

70. Real-world experience with cladribine in the MSBase Registry.

71. Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression

72. Natalizumab, Fingolimod, and Dimethyl Fumarate Use and Pregnancy-Related Relapse and Disability in Women With Multiple Sclerosis

73. The effectiveness of natalizumab vs fingolimod-A comparison of international registry studies

74. Brain atrophy and lesion burden are associated with disability progression in a multiple sclerosis real-world dataset using only T2-FLAIR: The NeuroSTREAM MSBase study

75. Associations of Disease-Modifying Therapies With COVID-19 Severity in Multiple Sclerosis

77. Real-world experience with ocrelizumab in the msbase registry

78. Pregnancy in a modern day multiple sclerosis cohort: predictors of postpartum relapse and disability progression

80. Pregnancy in a modern day multiple sclerosis cohort: predictors of relapse during pregnancy

83. of Therapeutic Lag in Relapsing Multiple Sclerosis

84. Predicting long-term sustained disability progression in multiple sclerosis.

85. Real-world experience with Ocrelizumab in the MSBase Registry.

86. Predicting long-term sustained disability progression in multiple sclerosis.

87. Delay from treatment start to full effect of immunotherapies for multiple sclerosis

89. Comparative effectiveness of natalizumab and fingolimod in different subgroups of patients with relapsing-remitting multiple sclerosis

95. Therapeutic lag in relapsing multiple sclerosis

96. Comparison of the effectiveness of rituximab versus alemtuzumab and natalizumab in active relapsing-remitting MS

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