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Record W1990436647 · doi:10.4061/2011/974514

Transcatheter Arterial Chemoembolization of Hepatocellular Carcinoma as a Bridge to Liver Transplantation: A Retrospective Study

2011· article· en· W1990436647 on OpenAlexaff
Antoine Bouchard‐Fortier, Réal Lapointe, Pierre Perreault, Louis‐S. Bouchard, Gilles Pomier–Layrargues

Bibliographic record

VenueInternational Journal of Hepatology · 2011
Typearticle
Languageen
FieldMedicine
TopicHepatocellular Carcinoma Treatment and Prognosis
Canadian institutionsCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsMedicineHepatocellular carcinomaLiver transplantationTranscatheter arterial chemoembolizationLipiodolMilan criteriaTransplantationGastroenterologyInternal medicineCarcinomaLiver functionSurgeryRadiology

Abstract

fetched live from OpenAlex

Background. Transcatheter arterial lipiodol chemoembolization (TACE) can be used in cirrhotic patients with hepatocellular carcinoma to avoid tumor progression before transplantation. Objective. To evaluate the efficacy and safety of TACE used as a bridge to liver transplantation. Methods. TACE was performed in 30 cirrhotic patients with hepatocellular carcinoma. Milan criteria were used to select patients for transplant. Patients had a good or moderately impaired liver function, no arterioportal fistulae, and a good portal perfusion. Results. 48 TACE were performed in 30 patients. Before transplantation, 4 patients were dropped off the list due to tumor extension or liver failure. Complete necrosis of the tumor was observed in 11 patients and partial necrosis in 15 patients. After transplantation, 6 patients died and tumor recurrence was observed in 5 patients with a tumor beyond Milan criteria or no response to TACE. Conclusion. TACE is useful as a bridge to liver transplantation in a selected group of cirrhotic patients with hepatocellular carcinoma. A combined therapeutic approach before surgery might improve the prognosis in 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 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.683

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)0.0010.000

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 head0.060
GPT teacher head0.277
Teacher spread0.217 · 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 designObservational
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

Citations28
Published2011
Admission routes1
Has abstractyes

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