Letter to: Real‐World Clinical Experience With Serum <scp>MOG</scp> and <scp>AQP4</scp> Antibody Testing by Live Versus Fixed Cell‐Based Assay
Notice bibliographique
Résumé
I read with interest this manuscript by Said et al. [1] which reports the sensitivity of anti-MOG and anti-AQP4 fixed CBA to be substantially lower than that of live CBA (approximately 50% lower for anti-MOG and 25% lower for anti-AQP4). This contrasts with several prior reports, which describe a more modest 10%–15% lower sensitivity of anti-MOG fixed CBA and comparable sensitivity of anti-AQP4 fixed CBA when compared to live CBA [2, 3]. The authors appropriately acknowledge these discrepancies and note that it is conceivable that differences in laboratory practices and training may be a contributor, even though fixed CBA was performed at a large academic center. Could the authors elaborate on fixed CBA testing at this center, including what instrumentation is used to run samples, whether manual or automated microscopy is used to read slides, the number of readers employed, and whether any grading of immunofluorescence (e.g., Weak Positive, Positive, 1+, 2+, etc.) is reported? The authors also state that testing by both fixed and live CBA was not performed for all patients with suspected demyelinating attacks, but that they would not expect this to significantly impact estimates of specificity/sensitivity because these measures are not dependent on disease prevalence in the tested population. However, estimates of specificity/sensitivity are susceptible to bias arising from suboptimal selection of the tested population [4]. The authors state that one typical scenario for testing samples by both fixed and live CBA was a persistently high index of suspicion despite negative fixed CBA testing locally. If the proportion of patients who underwent testing by both assays for this reason was high, then this would intuitively seem to be biased against the calculated sensitivity of fixed CBA relative to live CBA; it could enrich your tested population with patients who are negative by fixed CBA but positive by live CBA, and deplete your tested population of patients who are positive by fixed CBA but negative by live CBA. This potential bias may contribute to the significant discrepancy in the proportion of samples that were positive for anti-MOG by fixed CBA but negative by live CBA in their clinical testing cohort versus biobank cohort (1/552 [0.2%] versus 4/42 [9.5%], p = 0.0001 by Fisher's exact test). Could the authors elaborate on the indications for testing by both fixed and live CBA in their cohort, and in particular clarify what proportion of patients tested by both assays were initially negative by fixed CBA? A.B. contributed to drafting the manuscript. Adrian Budhram reports that he holds the London Health Sciences Centre and London Health Sciences Foundation Chair in Neural Antibody Testing for Neuro-Inflammatory Diseases. He receives support from the Opportunities Fund of the Academic Health Sciences Centre Alternative Funding Plan of the Academic Medical Organization of Southwestern Ontario (AMOSO). Data sharing is not applicable to this article as no new data were created or analyzed in this study.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».