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Record W2751417142 · doi:10.3233/jnd-170211

Obinutuzumab Plus Chlorambucil in a Patient with Severe Myasthenia Gravis and Chronic Lymphocytic Leukemia

2017· article· en· W2751417142 on OpenAlexaff
Angela Russell, Megan Yaraskavitch, Daniel Fok, Sameer Chhibber, Lesley Street, Lawrence Korngut

Bibliographic record

VenueJournal of Neuromuscular Diseases · 2017
Typearticle
Languageen
FieldMedicine
TopicMyasthenia Gravis and Thymoma
Canadian institutionsUniversity of Calgary
FundersMedical Research Council
KeywordsObinutuzumabChlorambucilMedicineChronic lymphocytic leukemiaMyasthenia gravisCD20RituximabInternal medicineLeukemiaImmunologyGastroenterologyOncologyChemotherapyLymphomaCyclophosphamide

Abstract

fetched live from OpenAlex

Myasthenia gravis (MG) is an autoimmune disease of the neuromuscular junction, characterized by fatigable weakness of the extraocular, bulbar, and limb musculature; prevalence is estimated at 14 to 32 per 100,000 in North America. Chronic lymphocytic leukemia (CLL) is the most common type of leukemia in adults, resulting from clonal expansion of B-cells in blood, marrow, and secondary lymphoid tissues. The simultaneous presentation of MG and CLL is exceedingly rare. This article presents the case of 71-year-old man diagnosed simultaneously with MG and CLL. His MG was severe and refractory to treatment; therefore, a strategy of treating his coexisting CLL with obinutuzumab and chlorambucil was pursued. Following 6 cycles of obinutuzumab and chlorambucil, his CLL is in remission and his MG is almost entirely undetectable. This is the first case report describing the use of obinutuzumab, a novel anti-CD20 monoclonal antibody, in a patient with concurrent MG and CLL.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.236
Teacher spread0.226 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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

Citations19
Published2017
Admission routes1
Has abstractyes

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