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Record W2808434159 · doi:10.1155/2018/3190548

The Continental Divide: Anti-TNF Use in Pediatric IBD Is Different in North America Compared to Other Parts of the World

2018· article· en· W2808434159 on OpenAlexaff
Peter Church, Jeffrey S. Hyams, Frank M. Ruemmele, Lissy de Ridder, Dan Turner, Anne M. Griffiths

Bibliographic record

VenueCanadian Journal of Gastroenterology and Hepatology · 2018
Typearticle
Languageen
FieldMedicine
TopicAcute Lymphoblastic Leukemia research
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineThiopurine methyltransferaseInfliximabUlcerative colitisInflammatory bowel diseaseInternal medicineMethotrexateTumor necrosis factor alphaGastroenterologyDisease

Abstract

fetched live from OpenAlex

Background and Aims: Use of anti-TNF therapies varies internationally. As an initiative of the international Pediatric IBD Network (PIBDNet), we compared global pediatric IBD anti-TNF practice patterns. Methods: , or related samples Wilcoxon signed rank tests were used to compare groups. Results: 344 physicians treating pediatric IBD responded from 43 countries (54% North America, 29% Europe, 6% Oceania, 6% Asia, 3% Africa, and 2% South America). Respondents treated a median 40 IBD patients. CD was more commonly treated with anti-TNF than UC (40% vs. 10%, p<0.001). North Americans more often used anti-TNF (median 50% vs. 30%, p<0.001) and before immunomodulator (80% vs. 35% CD, p<0.001; 76% vs. 43% steroid-dependent UC, p<0.001). Anti-TNF monotherapy was more common in North America. Anti-TNF in combination with methotrexate, instead of thiopurine, characterized North American practices. North Americans more often continued immunomodulator indefinitely and less often adhered to standard infliximab induction dosing. Access limitations were more common outside North America and Europe for both CD (67% vs. 31%, p<0.001) and UC (62% vs. 33%, p<0.001). Conclusions: Anti-TNF use in North America varies significantly from elsewhere.

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 score0.744
Threshold uncertainty score0.673

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.019
GPT teacher head0.259
Teacher spread0.240 · 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

Citations25
Published2018
Admission routes1
Has abstractyes

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