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Record W3186009020 · doi:10.1177/20552173211029674

A retrospective analysis of changes in lymphocyte levels in patients with multiple sclerosis during and after Tecfidera® treatment

2021· article· en· W3186009020 on OpenAlexaffabout
Maria-Elizabeth Baeva, Philip Boris Baev, Jill R. Nelson, Anna Kazimirchik, Galina Vorobeychik

Bibliographic record

VenueMultiple Sclerosis Journal - Experimental Translational and Clinical · 2021
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsBurnaby HospitalFraser Health
Fundersnot available
KeywordsDiscontinuationCD8Multiple sclerosisLymphocyteImmune systemMedicineCD4-CD8 RatioImmunologyInternal medicineT cellLymphocyte subsets

Abstract

fetched live from OpenAlex

BACKGROUND: There are currently no best practice recommendations for lymphocyte subset monitoring for patients with multiple sclerosis (pwMS) on disease-modifying therapies including Tecfidera® (dimethyl fumarate, DMF). However, there have been several cases of pwMS on DMF without severe lymphopenia who had high CD4:CD8 T cell ratios and went on to develop progressive multifocal leukoencephalopathy. OBJECTIVE: Our objective was to characterize the changes in immune profile during and after DMF treatment in pwMS. METHODS: A retrospective analysis of longitudinal data from 299 pwMS who have been treated with DMF at the Fraser Health Multiple Sclerosis Clinic in British Columbia, Canada. The blood test results were taken from January 1, 2013 to April 1, 2020. RESULTS: Our results suggest that CD8+ T cells had the highest proportional decrease compared to other lymphocyte subset populations and overall lymphocyte count in response to DMF treatment. CD56+ Natural Killer cells were similarly decreased in response to DMF treatment. CD4:CD8 T cell ratio was the measurement that had the highest rate of change in response to DMF initiation and discontinuation. CONCLUSION: CD8+ T cell count and CD4:CD8 T cell ratio may be a more sensitive measurement of the immune landscape of patients with MS on DMF.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.107
GPT teacher head0.343
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2021
Admission routes2
Has abstractyes

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