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Record W2770062840 · doi:10.1002/jmri.25888

Can MR enterography screen for perianal disease in pediatric inflammatory bowel disease?

2017· article· en· W2770062840 on OpenAlexaff
Zehour Alsabban, Nicholas Carman, Rahim Moineddin, Ryan Lo, Sebastian K. King, Jacob C. Langer, Thomas D. Walters, Anne M. Griffiths, Peter Church, Mary‐Louise C. Greer

Bibliographic record

VenueJournal of Magnetic Resonance Imaging · 2017
Typearticle
Languageen
FieldMedicine
TopicAnorectal Disease Treatments and Outcomes
Canadian institutionsSickKids FoundationToronto General HospitalHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsCoronal planeMedicineMagnetic resonance imagingSagittal planeNuclear medicineGold standard (test)Steady-state free precession imagingRadiologyInflammatory bowel diseaseKappaPopulationPathologyDiseaseMathematics

Abstract

fetched live from OpenAlex

Background Pediatric Crohn's disease is associated with perianal disease (PAD). Magnetic resonance enterography (MRE) assesses small bowel involvement in pediatric inflammatory bowel disease (PIBD). Pelvic MRI (P‐MRI) is the gold standard for assessing PAD. Purpose To determine if MRE can accurately detect PAD in PIBD, distinguishing perianal fistulae (PAF) from perianal abscesses (PAA), referenced against P‐MRI. Study Type Retrospective. Population Seventy‐seven PIBD patients, 27 females (mean age 14.1 years), with P‐MRI and MRE within 6 months. Field Strength/Sequence 1.5T and 3T; P‐MRI: sagittal fat suppressed (FS) T 2 fast spin‐echo (FSE), coronal short tau inversion recovery, axial T 1 FSE, coronal and axial postcontrast FS T 1 FSE; MRE: coronal balanced steady‐state free‐precession (SSFP), coronal cine SSFP, coronal and axial single‐shot T 2 FS, axial SSFP, coronal ultrafast 3D T 1 ‐weighted gradient echo FS (3D T 1 GE), axial diffusion‐weighted imaging, coronal and axial postcontrast 3D T 1 GE FS. Assessment Two radiologists independently, then by consensus, assessed randomized MRI exams, recording PAF number, location, and length; and PAA number, location, length, and volume. Sensitivity analysis used clinical disease as the gold standard, calculated separately for P‐MRI and MRE. Statistical Tests Comparing MRE and P‐MRI consensus data, sensitivity, specificity, positive, and negative predictive values (P/NPV) were calculated. Inter‐ and intrareader reliability were assessed using kappa statistics. Results P‐MRI and MRE were paired, detecting PAD in 73 patients, PAF in 63, and PAA in 31 P‐MRI. MRE sensitivities, specificities, PPV, and NPV were: PAD 82%, 100%, 100%, 23%; PAF 74%, 71%, 92%, 38%; PAA 51%, 85%, 69%, 72%; clinical 82%, 22%, 37%, 69%; clinical P‐MRI 96%, 8%, 37%, 80%. MRE interreader agreement for PAD was moderate (kappa = 0.51 [0.29–0.73]), fair for PAF and PAA. Data Conclusion Using a standard technique, MRE can detect PAD with high specificity and moderate sensitivity in PIBD, missing some PAF and small PAA. Level of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1638–1645.

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 score0.041
Threshold uncertainty score0.631

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 head0.009
GPT teacher head0.272
Teacher spread0.262 · 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
Published2017
Admission routes1
Has abstractyes

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