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Record W4391873563 · doi:10.1093/jcag/gwad061.095

A95 INCIDENCE OF LUMINAL GASTROINTESTINAL CANCERS IN PATIENTS WITH CIRRHOSIS: A SYSTEMATIC REVIEW AND META-ANALYSIS

2024· review· en· W4391873563 on OpenAlexaff
Manisha Jogendran, Katie Y. Zhu, Rohit Jogendran, Nasruddin Sabrie, Daljeet Chahal

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2024
Typereview
Languageen
FieldMedicine
TopicHepatocellular Carcinoma Treatment and Prognosis
Canadian institutionsUniversity of TorontoUniversity of British ColumbiaQueen's University
Fundersnot available
KeywordsCirrhosisMeta-analysisIncidence (geometry)Internal medicineMedicineGastroenterologyGastrointestinal cancerCancerColorectal cancer

Abstract

fetched live from OpenAlex

Abstract Background The global incidence of cirrhosis is increasing, as is the incidence of luminal gastrointestinal cancers. It is unknown, however, if cirrhosis itself is a predisposing factor for luminal gastrointestinal cancer. Such an association would have significant clinical implications, particularly for cancer screening prior to liver transplantation. Aims To investigate the incidence of luminal gastrointestinal cancers in patients with underlying cirrhosis. Methods An electronic search was conducted to study the incidence of luminal gastrointestinal cancers in patients with cirrhosis. Study-specific standardized incidence ratios (SIR) along with their corresponding 95% confidence intervals for both overall cancer incidence and luminal cancer incidence were analyzed using a random-effects model. Statistical heterogeneity was assessed using the Cochran’s Q test and I2 statistic. Subgroup analysis was performed based on cirrhosis etiology and location of luminal malignancy. Results We identified 5054 articles; 4 studies were selected for data extraction. The overall incidence of all cancers was significantly higher in patients with cirrhosis, with a SIR of 2.79 (95% CI 2.18–3.57). When stratified by cirrhosis etiology, the incidence of all cancers remained significantly elevated for alcohol (SIR 3.13, 95% CI 2.24–4.39), PBC (SIR 1.40, 95% CI 1.10–1.79), and unspecified cirrhosis (SIR 3.52, 95% CI 1.87–6.65). Unspecified cirrhosis had the greatest overall cancer risk followed by alcohol, and PBC cirrhosis (p ampersand:003C 0.01). Alcohol related cirrhosis was significantly associated with an increased incidence of luminal cancers (SIR 4.39, 95% CI 2.84–6.78). Specifically, the highest incidence was observed for oral cavity and pharyngeal cancer (SIR 10.44, 95% CI 8.90–12.24), followed by esophageal cancer (SIR 7.85, 95% CI 4.98–12.36), and colorectal cancer (SIR 2.43, 95% CI 1.62–3.66) (p ampersand:003C 0.01). PBC cirrhosis was significantly associated with an increased incidence of luminal cancers (SIR 2.11, 95% CI 1.10–4.04). However, subgroup analysis did not reveal a significant association with the incidence of any subgroups. Unspecified cirrhosis was significantly associated with an increased incidence of luminal cancers (SIR 2.60, 95% CI 1.65–4.09). Risk of oral cavity and pharynx cancers (SIR 4.75, 95% CI 2.18–10.33) were greatest, followed by esophageal (SIR 4.52, 95% CI 1.57–12.99), stomach (SIR 2.12, 95% CI 1.31–3.42), and colorectal cancer (SIR 1.57, 95% CI 1.14–2.16) (p = 0.03). Conclusions The incidence of luminal gastrointestinal cancer is increased amongst patients with cirrhosis. Oral cavity, pharyngeal and esophageal cancer had increased incidence across all cirrhosis etiologies compared to gastric and colorectal cancer. Therefore, increased screening of luminal cancers should be considered in this population. Funding Agencies None

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)0.009
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.025
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0160.032
Bibliometrics0.0070.008
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.042
GPT teacher head0.272
Teacher spread0.230 · 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.

Study designMeta-analysis
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

Citations0
Published2024
Admission routes1
Has abstractyes

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