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Record W4251629148 · doi:10.3410/f.727121335.793537532

Faculty Opinions recommendation of An updated histological classification system for multiple sclerosis lesions.

2017· dataset· en· W4251629148 on OpenAlexaff
Jennifer L. Gommerman, Valeria Ramaglia

Bibliographic record

VenueFaculty Opinions – Post-Publication Peer Review of the Biomedical Literature · 2017
Typedataset
Languageen
FieldComputer Science
TopicAI in cancer detection
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMicrogliaMultiple sclerosisMyelinPathologyLuxol fast blue stainLesionProteolipid protein 1MedicineImmunohistochemistryMyelin basic proteinPathogenesisDemyelinating diseaseCentral nervous systemImmunologyInflammationInternal medicine

Abstract

fetched live from OpenAlex

Multiple sclerosis is a complex and heterogeneous, most likely autoimmune, demyelinating disease of the central nervous system (CNS). Although a number of histological classification systems for CNS lesions have been used by different groups in recent years, no uniform classification exists. In this paper, we propose a simple and unifying classification of MS lesions incorporating many elements of earlier histological systems that aims to provide guidelines for neuropathologists and researchers studying MS lesions to allow for better comparison of different studies performed with MS tissue, and to aid in understanding the pathogenesis of the disease. Based on the presence/absence and distribution of macrophages/microglia (inflammatory activity) and the presence/absence of ongoing demyelination (demyelinating activity), we suggest differentiating between active, mixed active/inactive, and inactive lesions with or without ongoing demyelination. Active lesions are characterized by macrophages/microglia throughout the lesion area, whereas mixed active/inactive lesions have a hypocellular lesion center with macrophages/microglia limited to the lesion border. Inactive lesions are almost completely lacking macrophages/microglia. Active and mixed active/inactive lesions can be further subdivided into lesions with ongoing myelin destruction (demyelinating lesions) and lesions in which the destruction of myelin has ceased, but macrophages are still present (post-demyelinating lesions). This distinction is based on the presence or absence of myelin degradation products within the cytoplasm of macrophages/microglia. For this classification of MS lesions, identification of myelin with histological stains [such as luxol fast blue-PAS] or by immunohistochemistry using antibodies against myelin basic-protein (MBP) or proteolipid-protein (PLP), as well as, detection of macrophages/microglia by, e.g., anti-CD68 is sufficient. Active and demyelinating lesions may be further subdivided into the early and late demyelinating lesions. The former is defined by the presence in macrophages of major and small molecular weight myelin proteins, such as cyclic nucleotide diphosphoesterase (CNP), myelin oligodendrocyte glycoprotein (MOG), or myelin-associated protein (MAG), whereas macrophages in the latter demonstrate merely the presence of the major myelin proteins MBP or PLP. We discuss the histological features and staining techniques required to classify MS lesions, and, in addition, describe the histological hallmarks of cortical pathology and diffuse white matter changes, as well as of remyelination. PMID: 27988845 Funding information This work was supported by: Austrian Science Fund FWF, Austria Grant ID: I 2114

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)0.005
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.393
Threshold uncertainty score0.865

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.031
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0090.008
Science and technology studies0.0010.001
Scholarly communication0.0060.006
Open science0.0030.002
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.3930.311

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 head0.109
GPT teacher head0.371
Teacher spread0.262 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2017
Admission routes1
Has abstractyes

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