MétaCan
Menu
Back to cohort
Record W1485221450 · doi:10.14740/jocmr2174w

Pleural Epithelioid Hemangioendothelioma: Literature Summary and Novel Case Report

2015· article· en· W1485221450 on OpenAlexvenueno aff
Julita Salijevska, Robert E. Watson, Amy Clifford, Andrew I. Ritchie, Francesco Mauri, David Adeboyeku

Bibliographic record

VenueJournal of Clinical Medicine Research · 2015
Typearticle
Languageen
FieldMedicine
TopicVascular Tumors and Angiosarcomas
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineEpithelioid hemangioendotheliomaPresentation (obstetrics)BiopsyDifferential diagnosisRadiologyHemangioendotheliomaPleural effusionSurgeryPathology

Abstract

fetched live from OpenAlex

Epithelioid hemangioendothelioma (EHE) is a rare malignant cancer of vascular origin that can affect multiple and varied tissue sites. A subtype of EHE, pulmonary epithelioid hemangioendothelioma (PHE), is more unusual with only 200 reported cases. Of these, only 27 have been classified as pleural in origin. Based on available literature, the average age of presentation of pleural PHE is 45.7 years with a male preponderance of 2.375. A summary of all published case reports reveals significant heterogeneity both in presentation and management. Here we add to this knowledge-base with a report of an unusual case of pleural PHE in a 36-year-old female who presented with a 6-week history of chest pain and breathlessness. Significant challenges in the diagnosis and management of patients with pleural PHE exist, including a wide initial differential diagnosis and difficulties in obtaining tissue specimens, coupled with relatively limited treatment options. Early referral to a cardiothoracic center for video-assisted thoracoscopic biopsy is crucial in facilitating a diagnosis and allowing adequate pleural drainage for symptomatic relief.

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)0.002
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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.003
Science and technology studies0.0020.002
Scholarly communication0.0020.004
Open science0.0020.002
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0030.002

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 head0.329
GPT teacher head0.538
Teacher spread0.208 · 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

Citations25
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Clinical Medicine ResearchSame topicVascular Tumors and AngiosarcomasFrench-language works237,207