MétaCan
Menu
Back to cohort
Record W4410299773 · doi:10.1016/j.cgh.2025.04.014

Global Health Policies for Hepatocellular Carcinoma: A Cross-Sectional Study

2025· article· en· W4410299773 on OpenAlexaff
Douglas Chee, Christen En Ya Ong, Darren J. Tan, Jörn M. Schattenberg, George N. Ioannou, Daniel Q. Huang

Bibliographic record

VenueClinical Gastroenterology and Hepatology · 2025
Typearticle
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsCentre Hospitalier de l’Université de Montréal
FundersUniversidad AustralUniversity of California, San DiegoUniversitair Ziekenhuis AntwerpenFaculty of Medicine, Prince of Songkla UniversityCentre Hospitalier Universitaire de BordeauxUniversidad Nacional Mayor de San MarcosNanjing UniversityCollege of Medicine, King Saud UniversityUniversità di BolognaChinese University of Hong KongNational Medical Research CouncilUniversity of the PhilippinesNational and Kapodistrian University of AthensKarolinska InstitutetUniversity of California-San DiegoUniversiti MalayaHelwan UniversityUniversity of Health and Allied SciencesNational University of SingaporeKurume UniversityUniversiti Sains MalaysiaUniversidade Federal de São PauloMinistry of Health -SingaporeNanjing Drum Tower HospitalEge ÜniversitesiCedars-Sinai Medical CenterUniversity of Texas at San AntonioUniversity of ArizonaMakerere UniversityE-Da HospitalAarhus UniversitetshospitalGilead SciencesUniversitas IndonesiaYonsei University College of MedicineAddis Ababa UniversityRocheXi'an Jiaotong University
KeywordsMedicineHepatocellular carcinomaCross-sectional studyGlobal healthEnvironmental healthInternal medicineOncologyPublic healthPathology

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.054
GPT teacher head0.438
Teacher spread0.383 · 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 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

Citations3
Published2025
Admission routes1
Has abstractno

Explore more

Same venueClinical Gastroenterology and HepatologySame topicLiver Disease Diagnosis and TreatmentFrench-language works237,207