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Record W2078358256 · doi:10.1002/jmri.21186

MRI appearance of perianal carcinoma in Crohn's disease

2007· article· en· W2078358256 on OpenAlexaff
Masoom A. Haider, Carl J. Brown, Robin S. McLeod

Bibliographic record

VenueJournal of Magnetic Resonance Imaging · 2007
Typearticle
Languageen
FieldMedicine
TopicAnorectal Disease Treatments and Outcomes
Canadian institutionsSt. Paul's HospitalPrincess Margaret Cancer CentreUniversity Health NetworkUniversity of TorontoUniversity of British ColumbiaMount Sinai Hospital
Fundersnot available
KeywordsMedicineMucinous carcinomaAdenocarcinomaCrohn's diseaseRadiologyCarcinomaColorectal cancerDiseasePathologyCancerMagnetic resonance imagingInternal medicine

Abstract

fetched live from OpenAlex

Detection of carcinoma in perianal Crohn's disease can be difficult. The purpose of this study was to describe the MRI appearance of anorectal cancer in patients with perianal Crohn's disease. A total of six patients with anorectal carcinoma (four mucinous adenocarcinoma, two squamous) in Crohn's disease were retrospectively reviewed. Axial T2 and dynamic postcontrast fat-suppressed T1-weighted gradient echo sequences were performed, and findings were compared with 18 noncancer patients with perianal fistulae in Crohn's disease. MRI characteristics of carcinoma were irregular inner wall contours and delayed mild enhancement of internal tissue. The combined features of an irregular internal wall and delayed enhancing tissue were seen exclusively in cancer patients. The four cases of mucinous adenocarcinoma all displayed a pattern of lobulated fluid-filled cavities with delayed internal tissue enhancement. This pattern was not seen in any of the control cases. The presence of a double-layered enhancement pattern was seen in both cases of squamous carcinoma and in only one of four cases of mucinous adenocarcinoma and one of 18 noncancer cases. The pattern of contrast enhancement is valuable in the MRI diagnosis of carcinoma in perianal Crohn's disease.

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 score0.093
Threshold uncertainty score0.389

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 head0.007
GPT teacher head0.268
Teacher spread0.260 · 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

Citations29
Published2007
Admission routes1
Has abstractyes

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