MétaCan
Menu
← Back to cohort
Record W2412982400

[Relapse-Free Survival Following Multidisciplinary Therapy for Hepatocellular Carcinoma with Multiple Lung Metastases--A Case Report].

2015· article· ja· W2412982400 on OpenAlexaboutno aff
Hirofumi Hasegawa, Shigeru Ueda, Tomonori Nakanoko, Tomonobu Gion, Masayuki Kitamura, Fumihiro Tanaka

Bibliographic record

VenuePubMed · 2015
Typearticle
Languageja
FieldMedicine
TopicHepatocellular Carcinoma Treatment and Prognosis
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHepatocellular carcinomaChemotherapyLungMetastasisSurgical resectionHepatectomyOncologyCarcinomaInternal medicineResectionSurgeryRadiologyCancer
DOInot available

Abstract

fetched live from OpenAlex

Chemotherapy is not effective for metastatic hepatocellular carcinoma(HCC); however, prolonged survival can be expected for patients with multiple metastases who are treated with surgical resection in addition to chemotherapy. We present a case of a 36-year-old woman with hepatitis B who developed HCC with multiple intrahepatic and lung metastases after undergoing resection of HCC in 2010 in Canada. The patient returned to Japan for additional treatment. She was treated with TACE therapy and systemic chemotherapy, but her lung metastases did not improve. The patient's PIVKA-Ⅱ levels remained moderately elevated after initiation of chemotherapy. Therefore, we performed surgical resection of the lung metastases in March 2014. Five months later, the patient received additional TACE therapy when an isolated metastasis was found in the residual liver. Since then, no recurrence of HCC has been found, and the patient's PIVKA-Ⅱ levels have remained in the normal range. This case demonstrates that surgical resection for multiple lung metastases is possible in select 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 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)0.001
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.097
GPT teacher head0.268
Teacher spread0.171 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

Explore more

Same venuePubMed→Same topicHepatocellular Carcinoma Treatment and Prognosis→French-language works237,207→