<i>The Journal of Applied Laboratory Medicine</i> Special Issue on Autoimmune Diagnostics
Notice bibliographique
Résumé
Autoimmune diagnostics is a rapidly growing area within laboratory medicine. For many laboratorians, this field is filled with complex immunology concepts, unique technologies, and lack of standardization, leaving some thinking that the idea of being responsible for this area of testing is daunting. In the past, the core clinical chemistry curriculum glossed over autoimmune diagnostics, covering perhaps only basic immunology and specific immune responses to more traditional diseases. Within this special issue of JALM, we wished to cover both systemic autoimmune diseases including connective tissue diseases, myositis and rheumatoid arthritis, and organ-specific autoimmune diseases including autoimmune hepatitis, type 1 diabetes, and celiac disease, to name a few. As we assembled this special issue, we acknowledged that beyond the fundamentals of autoimmune diagnostics, we needed to cover some important practical considerations for the laboratory. This includes standardized reporting, utilization of autoantibodies, and both analytical and quality considerations. Finally, we wanted to look toward the future of this growing field and cover emerging methodologies and applications for autoantibodies—not yet ready for prime time but ones that certainly hold promise for transition into the clinical diagnostic laboratory. As such, we titled the JALM special issue as “Autoimmune Diagnostics: Fundamentals to Cutting Edge.” We are excited to bring together review articles from experts dealing with dermatologic diseases, autoimmune encephalitis, myositis antibodies and interstitial lung disease, common connective tissue diseases, immunodeficiency and autoantibodies to cytokines, autoantibodies in endocrine disease, and islet autoantibody testing in type 1 diabetes, to name a few. There are mini-review articles covering clinical, analytical, and practical considerations for algorithmic testing in autoimmune serology, multiinflammatory syndrome in children, psoriatic disease, pediatric celiac disease, and autoimmune liver disease. There are also review articles covering emerging methodologies to examine features of autoantibodies and detecting autoantibodies by multiparametric assays. Our packed special issue also includes technical tips on the utility of live cell-based assays for autoimmune neurology diagnostics, autoantibody testing in idiopathic inflammatory myopathies, and the use of specific software for interpretation of antinuclear antibody pattern and titer. We have a fantastic collection of opinion pieces from experts on antiphospholipid antibodies and revisiting the gold standard antinuclear antibody testing. Numerous primary articles, case reports, focused reports, and letters to the editor add depth and knowledge to this special issue. Our hope is that this issue will serve as an important resource for laboratorians to embrace this growing field and for our learners to understand and implement some of the strategies outlined. Author Contributions: All authors confirmed they have contributed to the intellectual content of this paper and have met the following 4 requirements: (a) significant contributions to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the article for intellectual content; (c) final approval of the published article; and (d) agreement to be accountable for all aspects of the article thus ensuring that questions related to the accuracy or integrity of any part of the article are appropriately investigated and resolved. Authors’ Disclosures or Potential Conflicts of Interest: Upon manuscript submission, all authors completed the author disclosure form. Disclosures and/or potential conflicts of interest: Employment or Leadership: V. Kulasingam, The Journal of Applied Laboratory Medicine, AACC; R.A. Booth, L.K. Peterson, and M.R. Snyder, guest editors, The Journal of Applied Laboratory Medicine, AACC. M.R. Snyder, Secretary of Association of Medical Laboratory Immunologists. Consultant or Advisory Role: V. Kulasingam, Abbott Laboratories. Stock Ownership: None declared. Honoraria: None declared. Research Funding: None declared. Expert Testimony: None declared. Patents: None declared.
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 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,002 |
| Bibliométrie | 0,006 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,006 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,007 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,036 | 0,024 |
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 ».