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Record W4391886319 · doi:10.1093/jcag/gwad061.311

A311 EFFICACY OF PANCREATIC CANCER SCREENING IN HIGH RISK INDIVIDUALS

2024· article· en· W4391886319 on OpenAlexaffabout
B Harjot, Dezhi Cheng, J Buttar, Eric Lam, Jennifer J. Telford

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2024
Typearticle
Languageen
FieldMedicine
TopicPancreatic and Hepatic Oncology Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPancreatic cancerMedicineInternal medicineOncologyCancer

Abstract

fetched live from OpenAlex

Abstract Background Pancreatic cancer (PC) is the 11th most prevalent cancer in Canada with about 6900 new cases in 2021. Due to late diagnosis, it is the 4th largest cause of cancer related mortality in Canada. Despite advances in diagnosis and treatment over the past decade, the 5-year survival remains ampersand:003C10%, making pancreatic cancer one of the most lethal malignancies worldwide. However, if detected early, the 5-year survival increases to 80%. The incidence of pancreatic cancer remains low. Therefore, the International Cancer of the Pancreas Screening (CAPS) consortium do not recommend screening everyone. Selective screening in high-risk individuals may be effective in early detection and treatment of pancreatic cancer. Aims 1. To determine the characteristics of patients undergoing PC screening at a single tertiary care center in Vancouver. 2. To determine the frequency of disease progression and outcomes in patients diagnosed with high-risk lesions during screening. Methods Retrospective chart review was performed for patients who were screened between January 1, 2005 to June 30, 2023. As per consensus from the CAPS Consortium, patients with at least 2 close relatives with PC, and those with predisposing genetic mutations screened using endoscopic ultrasound (EUS) and/or magnetic resonance imaging (MRI) were included in the study. Clinical data, such as age, tobacco use, genetic risk, endoscopic findings, and outcomes, such as disease progression, were collected. Statistical analysis was performed using Rstudio and Microsoft Excel. Results 73 patients were included, with 42 included due to pathogenic germline mutations, and 31 with familial pancreatic cancer. Most were female (52, 71.2%). Median age in the study was 54 years (range 24-74 years) at initial screening. 66 patients (90.4%) received an EUS and 7 patients (9.6%) received MRI for initial screening. At the time of initial screening. 74.6% of patients had a normal pancreas. 6 patients (8.2%) were found to have neoplastic lesions, e.g. ampulloma, neuroendocrine tumour or adenocarcinoma at initial screening and referred for surgery. In the median follow up time of 67.9 months (range 0-168.1 months), 6 patients were noted to have disease progression, 4 of which had increase in cyst size without any other worrisome features. 2 patients had progression to malignancy. 1 of those patients received chemotherapy and surgical resection and remains alive; the other patient was lost of follow up due to geographical move. Conclusions Pancreatic cancer screening in high risk individuals is effective in early detection and treatment. Given lower incidence of pancreatic cancer, we likely need additional patient enrolment to observe a significant improvement in patient outcomes. 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)0.001
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 score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.018
GPT teacher head0.307
Teacher spread0.289 · 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
Published2024
Admission routes2
Has abstractyes

Explore more

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