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Record W4389903281 · doi:10.1016/j.cgh.2023.12.006

Development of an MRI-Based Prediction Model for Anti-TNF Treatment Failure in Perianal Crohn’s Disease: A Multicenter Study

2023· article· en· W4389903281 on OpenAlexaff
Jeffrey D. McCurdy, Javeria Munir, Simon Parlow, Jacqueline Reid, Russell Yanofsky, Talal Alenezi, Joseph Meserve, Brenda Becker, Zubin Lahijanian, Anas Hussam Eddin, Ranjeeta Mallick, Tim Ramsay, Greg Rosenfeld, Ali Bessissow, Talat Bessissow, Vipul Jairath, Siddharth Singh, David H Bruining, Blair Macdonald

Bibliographic record

VenueClinical Gastroenterology and Hepatology · 2023
Typearticle
Languageen
FieldMedicine
TopicAnorectal Disease Treatments and Outcomes
Canadian institutionsWestern UniversityMcGill University Health CentreUniversity of British Columbia HospitalUniversity of OttawaMcGill UniversityOttawa Hospital
FundersPfizer
KeywordsMedicineCrohn's diseaseDiseaseMulticenter studyMagnetic resonance imagingInternal medicineCrohn diseaseRadiologyGastroenterologyRandomized controlled trial

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.015
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.018
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.061
GPT teacher head0.377
Teacher spread0.316 · 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 source (direct Gemma or distilled Codex), 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

Citations16
Published2023
Admission routes1
Has abstractno

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