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Record W3215687115 · doi:10.1002/cam4.4403

Cancer registry study of malignant hepatic vascular tumors: hepatic angiosarcomas and hepatic epithelioid hemangioendotheliomas

2021· article· en· W3215687115 on OpenAlexaff
Constanza Martínez, Jonathan Lai, Daryl Ramai, Antonio Facciorusso, Zu‐Hua Gao

Bibliographic record

VenueCancer Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicVascular Tumors and Angiosarcomas
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineHistopathologyMetastasisCancerPathologyHistologyDiseaseInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Malignant vascular tumors (MVTs) are rare and often misdiagnosed due to wide range of clinical presentations, varied histology, and exquisite imagining features. We aim to characterize two different types of MVTs of the liver: hepatic angiosarcomas (HA) and hepatic epithelioid hemangioendotheliomas (HEHE). METHODS: Data on HA and HEHE between 1975 and 2016 were extracted from the SEER database and analyzed. RESULTS: A total of 366 patients with HA were identified where 64.2% were male and 79% of White race. The median age at diagnosis was 64 ± 16.2 years. Distant metastasis was found in 24% of patients, regional disease in 22.1%, and localized disease in 21.3%. The median overall survival for HA was 2 months. For HEHE, 120 cases were identified, 32.5% were male and 80% of White race. The median age of diagnosis was 51 ± 16.8 years. Distant metastasis was found in 37.5% of patients, regional disease in 27.5%, and localized disease in 20%. The median overall survival was 182 months. CONCLUSION: Patients' demographics such as race, age, and gender may assist in elucidating distinct subtypes of MVTs. HA is an aggressive tumor despite intervention. Patients with HEHE tumors have significantly better survival compared to patients with HA. Further studies are needed to deepen our knowledge about the histopathology of these tumors, the outcomes of liver transplantation as a therapeutic alternative, and available molecular targets for MVTs.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.095
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
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 head0.019
GPT teacher head0.291
Teacher spread0.272 · 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

Citations12
Published2021
Admission routes1
Has abstractyes

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