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Evaluation of gall bladder carcinoma with dynamic magnetic resonance imaging and magnetic resonance cholangiopancreatography

2006· article· en· W2004639698 on OpenAlexaff
RK Kaza, Manpreet Singh Gulati, JD Wig, YK Chawla

Bibliographic record

VenueAustralasian Radiology · 2006
Typearticle
Languageen
FieldMedicine
TopicCholangiocarcinoma and Gallbladder Cancer Studies
Canadian institutionsGovernment of Canada
Fundersnot available
KeywordsMedicineMagnetic resonance imagingMagnetic resonance cholangiopancreatographyRadiologyPathologicalCarcinomaPathologyEndoscopic retrograde cholangiopancreatographyGastroenterology

Abstract

fetched live from OpenAlex

The objective of this study is to evaluate the efficacy of dynamic MRI with magnetic resonance cholangiopancreatography (MRCP) in the preoperative assessment of gall bladder carcinoma. Magnetic resonance imaging and MRCP were carried out in 15 patients with gall bladder carcinoma before surgery and the imaging findings correlated with surgical and pathological findings. Gall bladder carcinoma manifested as focal or diffuse wall thickening in 73% (11/15) and as a mass replacing the gall bladder in 27% (4/15). All tumours showed enhancement in the early phase, which persisted into the delayed phase. The sensitivity and specificity of MRI with MRCP in detecting hepatic invasion, lymph node metastasis and bile duct invasion was 87.5 and 86%, 60 and 90%, and 80 and 100%, respectively. Magnetic resonance imaging correctly diagnosed duodenal invasion in only 50% and in none of the two patients with peritoneal metastasis. In conclusion, dynamic MRI with MRCP is an accurate and a reliable method of showing gall bladder carcinoma and in assessing its local and regional extent as part of preoperative assessment.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.172
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.010
GPT teacher head0.247
Teacher spread0.237 · 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.

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

Citations37
Published2006
Admission routes1
Has abstractyes

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