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Record W2034320803 · doi:10.1055/s-0030-1247128

Hepatocellular Carcinoma: Epidemiology, Surveillance, and Diagnosis

2010· review· en· W2034320803 on OpenAlexaff
Morris Sherman

Bibliographic record

VenueSeminars in Liver Disease · 2010
Typereview
Languageen
FieldMedicine
TopicHepatocellular Carcinoma Treatment and Prognosis
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsHepatocellular carcinomaMedicineCirrhosisIncidence (geometry)HCCSEpidemiologyCarcinomaInternal medicineGastroenterologyIntensive care medicine

Abstract

fetched live from OpenAlex

Hepatocellular carcinoma (HCC) is increasing in incidence in many countries, and is the most common cause of death in patients with cirrhosis. With regular surveillance, small early HCC lesions can be identified. An algorithm has been developed that allows for diagnosis of these lesions. Very early HCC lesions have high cure rates with appropriate treatment. If all these factors are in place most HCCs can be cured. KEYWORDS Hepatocellular carcinoma - hepatitis B - hepatitis C - surveillance - ultrasonography - α-fetoprotein - desgamma carboxyprothrombin - AFP-L3

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.005
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.318
Teacher spread0.221 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations398
Published2010
Admission routes1
Has abstractyes

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