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Record W3120181216 · doi:10.3390/curroncol28010041

Stereotactic Body Radiation Therapy (SBRT) for a Patient with a Myocardial Metastasis: A Case Report

2021· article· en· W3120181216 on OpenAlexaffvenue
Aneesh Dhar, E. Donovan, Darryl P. Leong, Sebastién J. Hotte, Anand Swaminath

Bibliographic record

VenueCurrent Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicCardiac tumors and thrombi
Canadian institutionsMcMaster UniversityHamilton Health SciencesPopulation Health Research InstituteHealth Sciences CentreSunnybrook Health Science CentreJuravinski Cancer Centre
Fundersnot available
KeywordsMedicinePalpitationsRadiologyLesionMagnetic resonance imagingRadiation therapyMetastasisRadiation treatment planningNuclear medicineInternal medicinePathologyCancer

Abstract

fetched live from OpenAlex

Metastatic lesions of the heart are rare but have the potential to cause significant morbidity. We describe the case of a patient with renal cell carcinoma who presented with shortness of breath and palpitations and was found to have a metastatic myocardial lesion causing arrythmia. He received stereotactic body radiation therapy (SBRT) to alleviate symptoms and provide local control. SBRT planning was executed using a four-dimensional computed tomography (4DCT) scan to account for respiratory and cardiac motion. Images from a planning magnetic resonance imaging (MRI) scan and a gated diagnostic MRI scan of the heart were fused with the 4DCT to assist with delineating the tumour. A dose of 30 Gy in five fractions was delivered without incident. The patient's cardiac MRI at two months post-treatment showed stability of his cardiac lesion. He subsequently died of distant disease progression, without any recurrence of his cardiac symptoms. SBRT may be considered for patients who present with a symptomatic metastatic cardiac lesion.

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)0.003
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 score0.008
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.002
Science and technology studies0.0050.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0080.005
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 head0.068
GPT teacher head0.389
Teacher spread0.322 · 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
Published2021
Admission routes2
Has abstractyes

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