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Record W2000209714 · doi:10.1155/2015/715102

Real-Life Report on Chemoembolization Using DEBIRI for Liver Metastases from Colorectal Cancer

2015· article· en· W2000209714 on OpenAlexaff
Michael D. Stutz, Aline Mamo, David Valenti, Anaïs Hausvater, Tatiana Cabrera, Peter Metrakos, Prosanto Chaudhury, G. Steacy, Evgenia Garoufalis, Petr Kavan

Bibliographic record

VenueGastroenterology Research and Practice · 2015
Typearticle
Languageen
FieldMedicine
TopicHepatocellular Carcinoma Treatment and Prognosis
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMedicineColorectal cancerOncologyRadiologyCancerInternal medicineGeneral surgery

Abstract

fetched live from OpenAlex

Background. Transarterial chemoembolization (TACE) has been investigated in patients with liver metastases from colorectal cancer (LMCRC). Limited experience and available data suggest that TACE can achieve disease stabilization or improvement, even in heavily pretreated patients. Methods. Patients with LMCRC, ECOG 0-2, who failed at least 1 line of systemic chemotherapy, received embolizations with 2 mL of microspheres preloaded with 100 mg of irinotecan. Beads were delivered selectively into hepatic arteries. Primary endpoint was overall survival (OS), analyzed using the Kaplan-Meier method. Secondary endpoint was safety, assessed using CTCAE version 4.0. Results. 27 patients were treated using DEBIRI. Patient median age was 57 years (range was 45-82 years). The median number of total embolizations was 1.3 (range 1-3). The median OS was 5.4 months (95% CI; 1.1-22.7 months). The most reported postembolization events were nausea (8/27), vomiting (6/27), right upper quadrant pain (16/27), fatigue (9/27), and the development of ascites (6/27). 5/26 patients required hospitalization after TACE for severe pain. Hospitalization was also required for 1 case of allergic reaction and 1 case of infection. Conclusion. Our data suggest that TACE with DEBIRI could be efficacious in a palliative setting for patients with LMCRC, but they do not necessarily support routine use in clinical practice.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.336
Threshold uncertainty score0.437

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
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 head0.351
GPT teacher head0.438
Teacher spread0.086 · 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

Citations19
Published2015
Admission routes1
Has abstractyes

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