MétaCan
Menu
Back to cohort
Record W2000166310 · doi:10.1080/02841850802220092

Detection of cholangiocarcinoma with magnetic resonance spectroscopy of bile in patients with and without primary sclerosing cholangitis

2008· article· en· W2000166310 on OpenAlexaff
Nils Albiin, Ian C. P. Smith, Urban Arnelo, Bo Lindberg, Annika Bergquist, B. Dolenko, N. Bryksina, Tedros Bezabeh

Bibliographic record

VenueActa Radiologica · 2008
Typearticle
Languageen
FieldMedicine
TopicLiver Diseases and Immunity
Canadian institutionsNational Research Council CanadaNational Research Council Institute for Biodiagnostics
Fundersnot available
KeywordsMedicinePrimary sclerosing cholangitisMagnetic resonance imagingRadiologyGastroenterologyNuclear magnetic resonancePathology

Abstract

fetched live from OpenAlex

BACKGROUND: Early detection of cholangiocarcinoma (CC) is very difficult, especially in patients with primary sclerosing cholangitis (PSC) who are at increased risk of developing CC. PURPOSE: To evaluate 1H magnetic resonance spectroscopy ((1)H-MRS) of bile as a diagnostic marker for CC in patients with and without PSC. MATERIAL AND METHODS: The institutional review board approved the study, and all patients gave informed consent. Bile from 49 patients was sampled and investigated using 1H-MRS. MR spectra of bile samples from 45 patients (18 female; age range 22-87 years, mean age 57 years) were analyzed both conventionally and using computerized multivariate analysis. Sixteen of the patients had CC, 18 had PSC, and 11 had other benign findings. RESULTS: The spectra of bile from CC patients differed from the benign group in the levels of phosphatidylcholine, bile acids, lipid, and cholesterol. It was possible to distinguish CC from benign conditions in all patients with malignancy. Two benign non-PSC patients were misclassified as malignant. The sensitivity, specificity, and accuracy were 88.9%, 87.1%, and 87.8%, respectively. CONCLUSION: With 1H-MRS of bile, cholangiocarcinoma could be discriminated from benign biliary conditions with or without PSC.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.255

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.010
GPT teacher head0.196
Teacher spread0.186 · 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

Citations60
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueActa RadiologicaSame topicLiver Diseases and ImmunityFrench-language works237,207