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Record W2278322489 · doi:10.1002/cam4.619

Role of palliative radiotherapy in the management of mural cardiac metastases: who, when and how to treat? A case series of 10 patients

2016· article· en· W2278322489 on OpenAlexaff
Alireza Fotouhi Ghiam, Laura A. Dawson, Wael Abuzeid, Sarah Rauth, Raymond Jang, Eric Horlick, Andrea Bezjak

Bibliographic record

VenueCancer Medicine · 2016
Typearticle
Languageen
FieldMedicine
TopicCardiac tumors and thrombi
Canadian institutionsCredit Valley HospitalPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineRadiation therapyPalliative careCancerIncidence (geometry)Palliative treatmentRadiographyRadiologyInternal medicineNursing

Abstract

fetched live from OpenAlex

Cardiac metastases (CM), although a rare manifestation of metastatic cancer, are increasing in incidence with the improved prognosis and increased longevity of many patients with cancer. This condition may be life-threatening, especially for bulky rapidly growing tumors. Such cancer presentations may be amenable to palliative radiotherapy to improve symptoms and to prevent further cardiac function decline. Here, we report on our experience with 10 patients with mural CM who received radiotherapy (RT) to the heart with palliative intent. The radiation treatment was given in different clinical situations using different dose and fractionation, and with a variety of outcomes. Palliative RT was a reasonably effective treatment, leading to good radiographic response in five patients who were evaluable for radiologic response. The mean duration of response in responding patients was 6.3 months (range: 3-11 months). This report describing clinical dilemmas around CM radiation therapy summarizes the previous experiences with radiation in treatment of CM and may assist in the considerations of palliative treatment for these patients.

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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0020.001
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 head0.014
GPT teacher head0.285
Teacher spread0.271 · 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

Citations33
Published2016
Admission routes1
Has abstractyes

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