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Two decades of change: Tracking intrahepatic cholangiocarcinoma mortality in the United States from 1999 to 2020.

2025· article· en· W4410814758 on OpenAlexaff
Anoud Khan, Aryan Tareen, Syed Usama Ashraf, Imaan Shoaib Mufti, Syeda Neha Rasool, Rinad Akhtar

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicCholangiocarcinoma and Gallbladder Cancer Studies
Canadian institutionsLondon Health Sciences Centre
Fundersnot available
KeywordsMedicineIntrahepatic CholangiocarcinomaInternal medicineGeneral surgeryDemographyOncology

Abstract

fetched live from OpenAlex

e16197 Background: Intrahepatic cholangiocarcinoma (ICC) is the second most common primary liver malignancy, recognized for its uncommon yet highly aggressive nature. In the United States, the incidence of ICC has been steadily rising, with a projected rise of up to 99% expected by 2029. Correspondingly, the overall mortality has been increasing in the United States over the past two decades for ICC. The 5-year survival rate remains very low at 9%, with cases rising globally. Objective: This 21-year retrospective analysis aims to study the multifaceted nature of ICC's growing incidence and mortality trends across the U.S. from 1999 to 2020 stratifying for demographic and regional variables. Through a comprehensive assessment of trends across various variables, our study provides a thorough understanding of high-risk demographics associated with ICC, aiming to address disparities and advocate for policy planning to ensure equitable access to cancer care across the globe. Methods: ICC mortality data was abstracted from CDC-WONDER from 1999-2020. Age-Adjusted Mortality Rate (AAMR) per 100,000 population and Annual Percentage Change (APC) along with 95% confidence intervals (CI) were determined. Joinpoint regression analysis was employed to examine trends across various demographic and regional groups. Results: A total of 113,450 ICC-related deaths occurred between 1999 and 2020. The overall-ICC related AAMR steadily increased from 0.98 in 1999 to 2.04 in 2020 with an APC of 3.55. Males had higher AAMR (2.32) as compared to females (1.83). Non-Hispanic (NH) Asian or Pacific Islander had the highest AAMR (2.61) in 2019, with the highest APC (4.19) recorded in NH-Black or African American individuals. Mortality rates were highest in people aged 85+, with the highest APC (4.43) observed in people aged 45-54. Wisconsin, Minnesota, Connecticut, Washington, Hawaii, and Rhode Island had approximately double AAMR compared to states that fell in the lower 10 th percentile. Large metropolitan had the highest AAMR (4.28) compared to rural areas. Conclusions: ICC mortality rates continue to rise alongside a low 5-year survival rate. Investigating contributing factors is essential, in conjunction with the outlined trends. Addressing demographic and regional disparities can enhance early diagnosis and treatment, improving public health outcomes. Annual percent change (APC) and age-adjusted mortality rates (AAMR) for intrahepatic cholangiocarcinoma (ICC) per 1000,000 population in the United States; 1999-2020. 1999 AAMR 2020 AAMR APC (95% CI) Overall 0.98 2.04 3.55 Males 1.13 2.32 3.35 Females 0.89 1.83 3.61 NH-Asian/Pacific 1.48 2.32 2.02 NH-Black 0.82 2.00 4.19 NH-White 0.98 2.01 3.54 Hispanic/Latino 1.20 2.05 2.93 Age 45-54 0.58 1.33 4.43 Large Metro 1.99 4.20 3.60

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.249
GPT teacher head0.506
Teacher spread0.257 · 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
Published2025
Admission routes1
Has abstractyes

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