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Record W4285555395 · doi:10.21037/acr-22-6

Migration of Calypso beacon transponders for hepatic stereotactic body radiotherapy: a report of two cases

2022· article· en· W4285555395 on OpenAlexaff
Razan Amjad, Youstina Soliman, Michael A. Pereira, Alessandra Cassano-Bailey, Mark Vivian, Sankar Venkataraman, Maged N. F. Nashed

Bibliographic record

VenueAME Case Reports · 2022
Typearticle
Languageen
FieldMedicine
TopicHepatocellular Carcinoma Treatment and Prognosis
Canadian institutionsUniversity of ManitobaHealth Sciences Centre
Fundersnot available
KeywordsRadiation therapyMedicineRadiology

Abstract

fetched live from OpenAlex

Background: The Calypso 4-dimensional Localization System allows the delivery of high-dose of radiation to a target guided by the implanted transponders. Calypso beacons are used for prostate and liver tumors treated with stereotactic body radiation therapy (SBRT). Several risks associated with this procedure have been previously observed. Here, we report on two cases where Calypso soft tissue transponders migrated to the lung shortly after implantation in liver. Case Description: Two male patients with hepatocellular carcinoma (HCC) underwent insertion of Calypso beacons in liver under image-guidance in preparation for SBRT. Post-procedure images confirmed the presence of the transponders within the liver. However, few days after implant, further imaging revealed a missing marker, in each patient, that had migrated to the right lung. Patients were asymptomatic and SBRT was delivered uneventfully. Conclusions: This is the first report of migration of Calypso beacons from liver to lung. In order to reduce the risk of migration, a Doppler ultrasound (US) prior to insertion could be performed to ensure that the transponders are at a safe distance from blood vessels. Anchored Calypso beacons, currently approved for insertion in the lung, could be tested as a suitable alternative to soft tissue beacons with a lower risk of migration.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.565
Threshold uncertainty score0.869

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.051
GPT teacher head0.300
Teacher spread0.250 · 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.

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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