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Record W4288058956 · doi:10.4103/ijpm.ijpm_1451_20

Pulmonary carcinosarcoma with an aggressive heterologous angiosarcoma component

2022· article· en· W4288058956 on OpenAlexaff
Jonathan Keow, Richard Inculet, Robert Hammond, Cady Zeman-Pocrnich

Bibliographic record

VenueIndian Journal of Pathology and Microbiology · 2022
Typearticle
Languageen
FieldMedicine
TopicMetastasis and carcinoma case studies
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsCarcinosarcomaAngiosarcomaChondrosarcomaPathologyMedicineMalignancyOsteosarcomaSpindle cell sarcomaSarcomaSpindle cell carcinomaRhabdomyosarcomaLungCarcinomaInternal medicine

Abstract

fetched live from OpenAlex

Pulmonary carcinosarcomas are rare biphasic lung tumors comprised of malignant epithelial and malignant mesenchymal components. The most common heterologous sarcomatous elements are osteosarcoma, rhabdomyosarcoma, and chondrosarcoma; a heterologous angiosarcoma component in a pulmonary carcinosarcoma is exceedingly rare. We report a case of a pulmonary carcinosarcoma containing adenocarcinoma, squamous cell carcinoma, undifferentiated malignant spindle cell, and heterologous angiosarcoma components. The patient, a 64-year-old woman, had initially presented to medical attention with hemoptysis. Although the tumor was thought to be confined to the lung at resection (pT3N0), she developed multiple metastatic foci within 3 weeks of lobectomy and required the evacuation of an intraparenchymal left occipital hematoma secondary to a hemorrhagic intra-axial focus of metastatic carcinosarcoma. She died 6 weeks after her primary lung resection from rapidly progressive metastatic disease. We hope the description and discussion provided herein will further the medical community's understanding of this rare malignancy.

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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.013
GPT teacher head0.237
Teacher spread0.224 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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