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Examining the treatment journey of patients diagnosed with hepatocellular carcinoma (HCC) using retrospective real-world data in British Columbia, Canada.

2023· article· en· W4379336973 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

VenueJournal of Clinical Oncology · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsUniversity of British ColumbiaBC Cancer AgencySunnybrook Hospital
FundersEisai Canada
KeywordsMedicineHepatocellular carcinomaRetrospective cohort studyPharmacyPopulationMedical recordCancerLiver cancerHealth careInternal medicineFamily medicineEmergency medicineOncologyEnvironmental health

Abstract

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e16111 Background: Hepatocellular carcinoma (HCC) is a complex disease with treatments that may include locoregional therapy (LRT), systemic therapy and liver transplant. Using retrospective real-world data from a tertiary care hospital in British Columbia (BC). We examined characteristics, treatment pathways, survival and relevant healthcare costs of HCC patients. Methods: This was a retrospective, real-world data study using data from two existing chart review studies (called MOTION and HCC) and the BC Transplant database. The Canadian province of British Columbia has a population of 5.0 million who receive medical coverage under the Medical Services Plan. “MOTION” collected data on patients who received LRTs between 2004 and 2017, while “HCC” collected data on patients who received systemic therapies between 2000 and 2019. MOTION and HCC and Transplant data were linked and patients were assigned a unique study number and personal health information (PHI) was removed. Start and stop dates for LRT and systemic therapies were used to determine treatment patterns. Treatment costs were provided by BC Cancer’s Pharmacy department, medical oncology visits were assumed to be monthly while patients were on systemic treatment, LRT costs were based on departmental expenditures, and the cost of a liver transplant was provided by the regional health authority. Analyses used descriptive statistics for baseline characteristics, duration and costs (in 2021 Canadian dollars) and Kaplan-Meier curves for survival. Results: There were 417 and 413 patients from the HCC and MOTION databases respectively, in which 63 patients were in both (referred to as the “common” group). Median age (years) at diagnosis was 64, 62 and 62 for HCC, MOTION and common groups respectively, while the proportion who were female was 17.5%, 18.2% and 15.9%. Median overall survival (mOS) from diagnosis was 21, 33 and 28 months for the HCC, MOTION and common groups, respectively. From start of systemic treatment, mOS was 9 months for the HCC group, 29 months from first LRT for patients in the MOTION group, and 26 months for the common group from first treatment. Treatment and cost results were based only on the common group: mean length of follow-up from diagnosis was 34.6 ± 26.2 months and mean time from diagnosis to first treatment was 4.7 ± 4.2 months. DEB-TACE was most frequently the first LRT, often received 3 rounds of LRT before starting systemic treatment, in which sorafenib was given mostly as first line systemic therapy. When including only costs related to LRTs, systemic therapies, medical oncology visits and liver transplants, the mean cost per patient was $94,419, resulting in $51,649 mean cost per patient per year. Conclusions: The linkage of HCC-related LRT, systemic and transplant databases provided detailed results related to the treatment patterns, survival and costs of HCC patients in Canada.

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)0.002
metaresearch head score (Gemma)0.008
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 score0.037
Threshold uncertainty score0.268

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.011
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
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 head0.210
GPT teacher head0.357
Teacher spread0.147 · 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".

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Citations0
Published2023
Admission routes3
Has abstractyes

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