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Record W4390222755 · doi:10.1155/2023/6632030

Migration of an Inferior Vena Cava Tumor Thrombus during Renal Cell Carcinoma Resection

2023· article· en· W4390222755 on OpenAlexaff
Roupen Hatzakorzian, Andrea Blotsky, Albert Moore, Julien Vaillancourt, Pattra Mettasittigorn, Armen Aprikian, Steven B. Backman

Bibliographic record

VenueCase Reports in Anesthesiology · 2023
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsMcGill UniversitySt Mary's Hospital CentreRoyal Victoria HospitalMcGill University Health CentreRoyal Victoria Regional Health Centre
Fundersnot available
KeywordsMedicineInferior vena cavaRenal cell carcinomaNephrectomyThrombusCardiopulmonary bypassVentricleSurgeryRadiologyComplicationCardiologyKidneyInternal medicine

Abstract

fetched live from OpenAlex

Approximately 4%-10% of patients with renal cell carcinoma (RCC) have tumoral vascular invasion with resultant thrombi in the renal vein and in the inferior vena cava (IVC). The authors describe an interesting case of IVC tumor thrombus that migrated to the right cardiac chambers during RCC resection. The diagnosis was made by intraoperative transesophageal echocardiography (TEE), which revealed the presence of a free-floating thrombus between the right atrium (RA) and right ventricle (RV). The patient required an urgent sternotomy with cardiopulmonary bypass (CPB) for atrial thrombus removal prior to the completion of the nephrectomy. The patient made a full recovery and was discharged to a rehabilitation facility. These findings illustrate the importance of intraoperative TEE monitoring during nephrectomy and IVC thrombectomy. In this case, TEE allowed for the diagnosis of an unexpected complication necessitating prompt cardiac surgical management.

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)0.004
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.002
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 head0.025
GPT teacher head0.275
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 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

Citations7
Published2023
Admission routes1
Has abstractyes

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