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Record W3181732028 · doi:10.1097/mpg.0000000000003225

Pediatric Patients with a Dual Diagnosis of Inflammatory Bowel Disease and Chronic Recurrent Multifocal Osteomyelitis

2021· article· en· W3181732028 on OpenAlexaff
Molly J. Dushnicky, Karen Beattie, Tania Cellucci, Liane Heale, Mary Zachos, Mary Sherlock, Michelle Batthish

Bibliographic record

VenueJournal of Pediatric Gastroenterology and Nutrition · 2021
Typearticle
Languageen
FieldMedicine
TopicOsteomyelitis and Bone Disorders Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineChronic recurrent multifocal osteomyelitisAdalimumabInfliximabInflammatory bowel diseaseEtiologySulfasalazineDiseaseInternal medicineOsteomyelitisUlcerative colitisEpidemiologyPediatricsSurgeryOsteitis

Abstract

fetched live from OpenAlex

ABSTRACT: There is a paucity of information about the epidemiology, pathophysiology, and treatment of patients with a dual diagnosis of inflammatory bowel disease (IBD) and chronic recurrent multifocal osteomyelitis (CRMO). A retrospective chart review was performed of patients at McMaster Children's Hospital with a diagnosis of either IBD or CRMO, to identify those with the dual diagnosis over a 10-year period. A dual diagnosis was identified in seven patients. Most patients (6/7) had a diagnosis of IBD first and were subsequently diagnosed with CRMO. At the time of CRMO diagnosis, IBD treatment regimens included one or more of, sulfasalazine (1/6), infliximab (3/6), adalimumab (1/6), or no treatment (1/6). Although the etiology of the link remains unknown, there does not seem to be an association to a specific IBD subtype, age, or treatment. Our patient population demonstrated a response to biologic agents, specifically tumor necrosis factor-α inhibitors, as treatment for both conditions.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.239
Teacher spread0.232 · 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 designCase report
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

Citations22
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Pediatric Gastroenterology and NutritionSame topicOsteomyelitis and Bone Disorders ResearchFrench-language works237,207