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Record W4388900264 · doi:10.1111/ene.16152

Reply to letter in response to Rethinking the diagnosis of double‐seronegative myasthenia gravis

2023· letter· en· W4388900264 on OpenAlexaff
Rodrigo Martinez‐Harms, Carolina Barnett, Mónica Alcántara, Vera Bril

Bibliographic record

VenueEuropean Journal of Neurology · 2023
Typeletter
Languageen
FieldMedicine
TopicMyasthenia Gravis and Thymoma
Canadian institutionsToronto General HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMyasthenia gravisMedicineNeostigmineRepetitive nerve stimulationEdrophoniumNeuromuscular transmissionElectromyographyPediatricsInternal medicinePhysical medicine and rehabilitation

Abstract

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We wish to thank Drs. Seok, Kim, Eun, and Lee for their valuable comments about our recently published original article “Clinical Characteristics and Treatment Outcomes in Patients with Double-Seronegative Myasthenia Gravis” [1]. We are very glad to see that our paper is stimulating further discussion on the topic of patients without detectable antibodies to nicotinic acetylcholine receptor (AChR) and muscle-specific kinase. The authors correctly point out that we found a significant clinical improvement in double-seronegative myasthenia gravis (dSNMG) patients in the last clinical evaluation, based on the Myasthenia Gravis Impairment Index and Single Simple Question [2, 3] scores strongly suggesting an immune-based mechanism and the benefit of immunotherapy in this group of patients. dSNMG represents a heterogeneous group, and the diagnosis is challenging and commonly based on the clinical presentation and progression. It is fundamental to exclude differential diagnoses such as congenital myasthenia gravis, muscular dystrophies, or other disorders of neuromuscular transmission. The authors think that the electrophysiological findings based on single fiber electromyography and repetitive nerve stimulation are essential in the diagnosis of myasthenia gravis (MG) [4]. There are additional complementary diagnosis tests for MG, for example, the edrophonium and neostigmine tests, which can be reliable, but are rarely used in clinical practice. Therefore, we did not use these tests as an inclusion criterion in our study. We agree fully with Seok et al. that patients who have refractory seronegative MG should be extensively reviewed for the possibility of alternate diagnoses, although we understand that refractoriness alone does not exclude a diagnosis, as seropositive MG patients can be refractory to treatment. There is a need for further characterization of serological markers in seronegative MG patients. Novel antibodies have been associated with MG and could be present in this group of patients. Especially relevant is the lipoprotein receptor-related protein 4 (LRP4), which may be an MG marker. One weakness of our study is the absence of LRP4 antibody serology characterization, which might have reduced the numbers considered to be dSNMG. Additional antibodies against other extracellular or intracellular targets have been found in some MG patients, such as agrin, collagen Q, Kv 1.4 potassium channels, titin, the ryanodine receptor, and cortactin, and these may play a role in MG [5]. Nevertheless, whether these antibodies produce disease pathology or are just an epiphenomenon needs to be further studied. Some of these antibodies have been found in healthy controls or associated with other immune conditions like cortactin antibodies, present in 20% of those with poliomyelitis [4]. It may be that dSNMG patients have one of these potential new markers, and this association hypothetically might be associated with a variation in the classic MG treatment response and have a poor response compared to classic AChR-positive MG. In conclusion, dSNMG patients have significant clinical improvement after treatment, supporting an immune-mediated pathophysiology and encouraging efforts to improve the response to treatment. The development of new biomarkers is promising and may improve personalized clinical characterization and individualized therapeutic approaches. Rodrigo Martinez-Harms: Conceptualization; writing – original draft; validation; writing – review and editing; supervision; project administration; investigation. Carolina Barnett: Conceptualization; writing – review and editing; supervision. Monica Alcantara: Conceptualization; writing – review and editing; supervision. Vera Bril: Conceptualization; writing – review and editing; supervision; project administration; writing – original draft. None of the authors has any conflict of interest to disclose. The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

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)0.003
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.029
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.036
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0020.006
Open science0.0030.002
Research integrity0.0290.036
Insufficient payload (model declined to judge)0.0060.006

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 head0.047
GPT teacher head0.287
Teacher spread0.240 · 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 designNot applicable
Domainnot available
GenreCommentary

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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