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Record W3024500864 · doi:10.1177/0846537120923273

Gallbladder Cancer: Imaging Appearance and Pitfalls in Diagnosis

2020· article· en· W3024500864 on OpenAlexaff
Susan John, Terence Moyana, Wael Shabana, Cindy Walsh, Matthew D. F. McInnes

Bibliographic record

VenueCanadian Association of Radiologists Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicCholangiocarcinoma and Gallbladder Cancer Studies
Canadian institutionsUniversity of OttawaOttawa Hospital
Fundersnot available
KeywordsMedicineGallbladder cancerGallbladderMalignancyRadiologyCholecystitisCancerGallbladder diseaseInternal medicine

Abstract

fetched live from OpenAlex

Gallbladder cancer is an uncommon malignancy with an overall poor prognosis. The clinical and imaging presentation of gallbladder cancer often overlaps with benign disease, making diagnosis difficult. Gallbladder cancer is most easily diagnosed on imaging when it presents as a mass replacing the gallbladder. At this stage, the prognosis is usually poor. Recognizing the features of gallbladder cancer early in the disease can enable complete resection and improve prognosis. Recognition of the patterns of wall enhancement on computed tomography can help differentiate gallbladder cancer from benign disease. Gallbladder wall thickening without pericholecystic fluid presenting in an older patient with raised alkaline phosphatase should raise concern regarding gallbladder cancer. Gallbladder polyps in high-risk individuals need close surveillance or surgery as per guidelines. Small gallbladder cancers in the neck can present as biliary dilatation or cholecystitis, and careful examination of this area is needed to assess for lesion. The imaging appearance of gallbladder cancer is reviewed and supported by local institutional data. Features that differentiate it from its common mimics enabling earlier diagnosis are described.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.131
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

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

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 head0.258
Teacher spread0.240 · 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 teacher head, 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

Citations13
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Association of Radiologists JournalSame topicCholangiocarcinoma and Gallbladder Cancer StudiesFrench-language works237,207