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Record W4405863341 · doi:10.1186/s12969-024-01053-4

Towards a histological diagnosis of childhood small vessel CNS vasculitis

2024· article· en· W4405863341 on OpenAlexaff
Maryam Nabavi Nouri, Anastasia Dropol, Pascal N. Tyrrell, Saira Sheikh, Marinka Twilt, Jean Michaud, Benjamin Ellezam, Harvey B. Sarnat, Christopher Dunham, Peter W. Schütz, Julia Keith, David G. Muñoz, Harry V. Vinters, Cynthia Hawkins, Susanne M. Benseler

Bibliographic record

VenuePediatric Rheumatology · 2024
Typearticle
Languageen
FieldMedicine
TopicVasculitis and related conditions
Canadian institutionsHealth Sciences CentreSt. Michael's HospitalSunnybrook Health Science CentreChildren's & Women's Health Centre of British ColumbiaHospital for Sick ChildrenUniversité de MontréalCentre Hospitalier Universitaire Sainte-JustineUniversity of OttawaAlberta Children's HospitalChildren's Hospital of Eastern OntarioUniversity of CalgaryUniversity of British ColumbiaWestern UniversityUniversity of TorontoSickKids Foundation
Fundersnot available
KeywordsMedicineVasculitisPathologyBrain biopsyBiopsyInflammationWhite matterInternal medicineDiseaseRadiologyMagnetic resonance imaging

Abstract

fetched live from OpenAlex

BACKGROUND: Primary small vessel CNS vasculitis (sv-cPACNS) is a challenging inflammatory brain disease in children. Brain biopsy is mandatory to confirm the diagnosis. This study aims to develop and validate a histological scoring tool for diagnosing small vessel CNS vasculitis. METHODS: A standardized brain biopsy scoring instrument was developed and applied to consecutive full-thickness brain biopsies of pediatric cases and controls at a single center. Stains included immunohistochemistry and Hematoxylin & Eosin. Nine North American neuropathologists, blinded to patients' presentation, diagnosis, and therapy, scored de-identified biopsies independently. RESULTS: A total of 31 brain biopsy specimens from children with sv-cPACNS, 11 with epilepsy, and 11 with non-vasculitic inflammatory brain disease controls were included. Angiocentric inflammation in the cortex or white matter increases the likelihood of sv-cPACNS, with odds ratios (ORs) of 3.231 (95CI: 0.914-11.420, p = 0.067) and 3.923 (95CI: 1.13-13.6, p = 0.031). Moderate to severe inflammation in these regions is associated with a higher probability of sv-cPACNS, with ORs of 5.56 (95CI: 1.02-29.47, p = 0.046) in the cortex and 6.76 (95CI: 1.26-36.11, p = 0.025) in white matter. CD3, CD4, CD8, and CD20 cells predominated the inflammatory infiltrate. Reactive endothelium was strongly associated with sv-cPACNS, with an OR of 8.93 (p = 0.001). Features reported in adult sv-PACNS, including granulomas, necrosis, or fibrin deposits, were absent in all biopsies. The presence of leptomeningeal inflammation in isolation was non-diagnostic. CONCLUSION: Distinct histological features were identified in sv-cPACNS biopsies, including moderate to severe angiocentric inflammatory infiltrates in the cortex or white matter, consisting of CD3, CD4, CD8, and CD20 cells, alongside reactive endothelium with specificity of 95%. In the first study of its kind proposing histological criteria for evaluating brain biopsies, we aim to precisely characterize the type and severity of the inflammatory response in patients with sv-cPACNS; this can enable consolidation of this population to assess outcomes and treatment methodologies comprehensively.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.139
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.244
Teacher spread0.232 · 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 teacher head, not a consensus.

Study designObservational
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

Citations2
Published2024
Admission routes1
Has abstractyes

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