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Record W2562322949 · doi:10.1155/2017/8192150

Metastatic Crohn’s Disease: An Approach to an Uncommon but Important Cutaneous Disorder

2017· review· en· W2562322949 on OpenAlexaff
Babak Aberumand, John M. Howard

Bibliographic record

VenueBioMed Research International · 2017
Typereview
Languageen
FieldMedicine
TopicAutoimmune and Inflammatory Disorders
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineDiseaseDermatologyCrohn's diseaseGastrointestinal tractRefractory (planetary science)BiopsySkin biopsyPathologyInternal medicine

Abstract

fetched live from OpenAlex

Objective. To provide physicians with a clinical approach to metastatic Crohn’s disease (MCD).Main Message. Metastatic Crohn’s disease, defined as skin lesions present in areas noncontiguous with the gastrointestinal tract, is the rarest cutaneous manifestation of Crohn’s disease. MCD lesions vary in morphology and can arise anywhere on the skin. MCD presents equally in both sexes and across age groups. Cutaneous findings may precede, develop concurrently with, or follow gastrointestinal involvement. A detailed history and thorough physical examination including a full-skin exam may help to exclude other dermatoses, as MCD can mimic other common disorders. A biopsy is required for a definitive diagnosis. Treatment options for MCD remain underwhelming due to the lack of randomized control studies and varying responses of reported therapeutic methods. Topical, intralesional, and systemic corticosteroids, antibiotics, traditional immunosuppressants, and surgery have shown mixed results. Recently, biologics have shown promise, even with refractory cases of MCD.Conclusion. MCD is an important cutaneous manifestation of this inflammatory disorder. Although a rare entity, early recognition can provide opportunity for successful therapeutic intervention.

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 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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.259
GPT teacher head0.496
Teacher spread0.236 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations57
Published2017
Admission routes1
Has abstractyes

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