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Record W2019645362 · doi:10.5414/npp29262

Tubuloreticular inclusions in inclusion body myositis

2010· article· en· W2019645362 on OpenAlexaff
Hans Katzberg, David G. Muñoz

Bibliographic record

VenueClinical Neuropathology · 2010
Typearticle
Languageen
FieldMedicine
TopicInflammatory Myopathies and Dermatomyositis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsInclusion body myositisPathologyMyocyteCytoplasmElectron microscopeInclusion bodiesImmunostainingCytoplasmic inclusionVacuoleMyositisMuscle biopsyStainingEndoplasmic reticulumAnatomyBiologyBiopsyChemistryMedicineCell biologyImmunohistochemistryBiochemistry

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate whether patients with inclusion body myositis (IBM) can have tubuloreticular inclusions present in muscle endothelial cells. MATERIAL AND METHODS: Light microscopy with histochemical staining and electron microscopy of a right quadriceps muscle biopsy were used to identify the pathological features in an 83-year-old patient with a clinical diagnosis of IBM. RESULTS: Light microscopy showed rimmed vacuoles. Immunostaining for HLA-1 revealed widespread membrane labeling and for TDP-43 multiple areas of subsarcolemmal and sarcoplasmic staining. Electron microscopy revealed tubuloreticular inclusions in the cytoplasm of endothelial cells. Electron microscopy also showed the presence of myeloid bodies and aggregates of tubolo filaments in the nucleus and cytoplasm of myocytes which confirmed the diagnosis of inclusion body myositis. CONCLUSION: Tubuloreticular inclusions may be found in the muscle endothelial cells of patients with a clinical and pathological diagnosis of IBM.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.340
Teacher spread0.327 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2010
Admission routes1
Has abstractyes

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