Reply to letter in response to Rethinking the diagnosis of double‐seronegative myasthenia gravis
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,036 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,002 | 0,006 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,029 | 0,036 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,006 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».