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Record W3042789112 · doi:10.1177/1066896920941939

Collision Lesions of Calcifying Pseudoneoplasm of the Neuraxis and Rheumatoid Nodules: A Case Report With New Pathogenic Insights

2020· article· en· W3042789112 on OpenAlexaff
Jian‐Qiang Lu, Snežana Popović, John Provias, Aleksa Cenic

Bibliographic record

VenueInternational Journal of Surgical Pathology · 2020
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal synovial abnormalities and treatments
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicinePathologicalPathologyMagnetic resonance imagingLesionBiopsyPathogenesisCalcificationRadiology

Abstract

fetched live from OpenAlex

Calcifying pseudoneoplasm of the neuraxis (CAPNON) is a rare tumor-like lesion with unclear pathogenesis. Collision lesions of CAPNONs with neoplasms are occasionally reported. In this article, we report the first case of collision lesions between CAPNON and rheumatoid nodules (RNs) in a patient with systemic lupus erythematosus. The patient was a 51-year-old female who presented with lower back pain and subsequently a lower back mass over 2 years. Spinal magnetic resonance imaging demonstrated a heterogeneous, partially calcified mass centered in the L3-4 paravertebral regions. A biopsy of the mass was diagnostic of CAPNON. As the mass grew over the following 5 months, it was resected en bloc. Its pathological examination revealed collision lesions of RNs at different histopathological stages and CAPNON lesions, and transitional lesions exhibiting combined RN and CAPNON features, with immune cell infiltrates. Our findings provide new evidence for an immune-mediated reactive process and insights into the pathogenies of CAPNON.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score0.226

Codex and Gemma teacher scores by category

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

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 head0.022
GPT teacher head0.279
Teacher spread0.257 · 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.

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

Citations13
Published2020
Admission routes1
Has abstractyes

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