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Record W1925039450 · doi:10.5858/2006-130-1219-mnopec

Malignant Neoplasm of Perivascular Epithelioid Cells of the Liver

2006· article· en· W1925039450 on OpenAlexaff
Jeremy Parfitt, Anthony J Bella, Jonathan I. Izawa, Bret Wehrli

Bibliographic record

VenueArchives of Pathology & Laboratory Medicine · 2006
Typearticle
Languageen
FieldMedicine
TopicTuberous Sclerosis Complex Research
Canadian institutionsLondon Health Sciences Centre
Fundersnot available
KeywordsAngiomyolipomaLymphangioleiomyomatosisPerivascular Epithelioid CellPathologyEpithelioid cellTuberous sclerosisClear cellImmunohistochemistryBiologyLungNeoplasmMedicineKidneyInternal medicine

Abstract

fetched live from OpenAlex

Neoplasms of perivascular epithelioid cells (PEComas) have in common the coexpression of muscle and melanocytic immunohistochemical markers. Although this group includes entities with distinct clinical features, such as angiomyolipoma, clear cell sugar tumor of the lung, and lymphangioleiomyomatosis, similar tumors have been documented in an increasing diversity of locations. The term PEComa is now generally used in reference to these lesions that are not angiomyolipomas, clear cell sugar tumors, or lymphangioleiomyomatoses. While most reported PEComas have behaved in a benign fashion, malignant PEComas have occasionally been documented. We present a case of hepatic PEComa with benign histologic features, which nonetheless presented with metastases to multiple sites nearly 9 years later. This case represents the second documented malignant PEComa of the liver, as well as the longest follow-up of a surviving patient with a malignant PEComa, emphasizing both the need for criteria that more accurately predict the behavior of PEComas and the necessity of long-term follow-up of patients with PEComas.

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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.001

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 head0.014
GPT teacher head0.246
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 source (direct Gemma or distilled Codex), 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

Citations122
Published2006
Admission routes1
Has abstractyes

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