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Record W4392810821 · doi:10.2217/hep-2023-0004

Treatment Journey of Patients with Hepatocellular Carcinoma Using Real-World Data in British Columbia, Canada

2023· article· en· W4392810821 on OpenAlexafffundabout
Soo Jin Seung, Hasnain Saherawala, Brandon Zagorski, Carman Tong, Howard J. Lim, Peter Kim, Vladimir Marquez, Sharlene Gill, David Liu, Janine M. Davies

Bibliographic record

VenueHepatic Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicHepatocellular Carcinoma Treatment and Prognosis
Canadian institutionsUniversity of British ColumbiaBC Cancer AgencyProvincial Health Services AuthorityVancouver General HospitalSunnybrook Health Science Centre
FundersAstellas PharmaEisaiIpsenAstraZeneca CanadaSunnybrook Research InstituteIncyteBoston Scientific CorporationBC Cancer AgencyAstraZenecaEisai CanadaSirtex MedicalAmgen
KeywordsMedicineHepatocellular carcinomaPsychological interventionLiver cancerCancerLiver diseaseDiseaseLiver transplantationIntensive care medicineInternal medicineFamily medicineTransplantationNursing

Abstract

fetched live from OpenAlex

Aim: This study examined treatment patterns, survival outcomes and healthcare costs related to hepatocellular carcinoma (HCC) in British Columbia. Methods: The study utilized data from two physician databases (HCC and MOTION) and the provincial British Columbia transplant database. Results: The analysis revealed diverse treatment approaches and identified the varying treatment journeys of patients. Liver transplant and systemic therapies demonstrated improved survival rates. However, there was a scarcity of Canadian-specific cost data. Conclusion: The research emphasizes the complexities of managing HCC and underscores the need for personalized treatment strategies to enhance patient outcomes. These findings contribute valuable insights into HCC management and provide a foundation for future studies and interventions aimed at optimizing care and resource allocation.

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)0.005
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 score0.030
Threshold uncertainty score0.220

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.008
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.097
GPT teacher head0.295
Teacher spread0.198 · 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

Citations0
Published2023
Admission routes3
Has abstractyes

Explore more

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