MétaCan
Menu
Back to cohort
Record W1516822793 · doi:10.1002/jso.21798

Neoadjuvant radiotherapy and reconstruction using autologous vein graft for the treatment of inferior vena cava leiomyosarcoma

2010· article· en· W1516822793 on OpenAlexaff
Gitonga Munene, Lloyd A. Mack, Randy D. Moore, Walley Temple

Bibliographic record

VenueJournal of Surgical Oncology · 2010
Typearticle
Languageen
FieldMedicine
TopicSarcoma Diagnosis and Treatment
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineLeiomyosarcomaInferior vena cavaSurgeryRadiation therapyRadiologyVein

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: Inferior vena cava (IVC) leiomyosarcomas are rare and are a relatively small subset of retroperitoneal sarcomas. The current approach is resection and ligation or reconstruction of the IVC. This study was undertaken to analyze the outcomes associated with the use of neoadjuvant radiotherapy and IVC reconstruction in the treatment of IVC leiomyosarcoma. METHODS: A retrospective clinicopathological review of patients treated during a 10-year period. RESULTS: Four patients were treated with neoadjuvant radiotherapy, median 47.5 Gy, all underwent margin negative resection with 75% of the tumors being high grade and all patients requiring resection of adjacent organs. Reconstruction of the IVC was performed with an autologous superficial femoral vein graft. There were no mortalities and the morbidity rate was 50%. At a median follow up of 37 months; two patients had a patent IVC, no patients had a local recurrence, and one patient developed a distant metastases treated successfully with metastectomy. CONCLUSIONS: Neoadjuvant radiotherapy and resection of the IVC leiomyosarcoma resulted in 100% local control, and all patients are alive at median follow up of 37 months. IVC reconstruction with the superficial femoral vein is safe and associated with acceptable short and long term morbidity.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.913
Threshold uncertainty score0.255

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.034
GPT teacher head0.346
Teacher spread0.312 · 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 designOther design
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

Citations27
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Surgical OncologySame topicSarcoma Diagnosis and TreatmentFrench-language works237,207