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Determinants of corticosteroid-resistance in severe pediatric ulcerative colitis

2009· article· en· W2314199142 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInflammatory Bowel Diseases · 2009
Typearticle
Languageen
FieldMedicine
TopicMicroscopic Colitis
Canadian institutionsIWK FoundationMount Sinai HospitalAgricultural Research Institute of OntarioHospital for Sick Children
Fundersnot available
KeywordsMedicineUlcerative colitisCorticosteroidGlucocorticoidInflammatory bowel diseaseProspective cohort studyInternal medicineCytokineLogistic regressionDiseaseImmunologyGastroenterology

Abstract

fetched live from OpenAlex

One third of children admitted for acute severe ulcerative colitis (UC), are refractory to intravenous corticosteroids and require second line therapy or colectomy. Potential factors determining response vs. refractoriness have seldom been explored. In an attempt to elucidate the basis for corticosteroid-resistance in UC, we evaluated whether corticosteroid-bioactivity and/or specific inflammatory cytokines influence response to corticosteroids in severe pediatric UC, independent of disease severity. In a prospective multi-center study, serum samples were obtained on the third day of steroid treatment from 79 children hospitalized with severe UC. Twenty three (29%) required second line therapy (mean age 13.9±3; 56% males; median disease duration 8.2 months (IQR 3-29)). Clinical, demographic and outcome data were prospectively recorded at several times during the admission on standardized case report forms. A cytokine antibody array panel (version 3.0; TransSignal, Panomics, Fremont, CA) was constructed to include 12 cytokines, se-lected based on a systematic literature search: TNF-α INF-γ IL-1β IL-2, IL-4, IL-5, IL-6, IL-8, IL-10, IL-12, IL-13, and IL-17. Factor analysis and logistic regression models were used to explore the relationship between the various cytokines and corticosteroid response. Biologic activity of corticosteroids was assessed using a previously established transactivation glucocorticoid bioassay (GBA) on COS-1 tranfected cells. GBA can measure the biological activity of corticosteroids by quantifying glucocorticoid response elements, reflecting the down-stream effect of the steroids. This approach eliminates differences between the various steroids regarding their ability to activate the glucocorticoid receptor. In univariate analyses, only IL-6 differed between responders and nonresponders (P= 0.003; Bonferroni corrected). The risk for steroid-refractoriness increased by 40% per each incremental unit of IL-6. In factor analysis, IL-6 loaded with IL-17 on the same component, reflecting the association of IL-6 with the Th17 pathway. However, in a multiple regression analysis IL-6 was no longer significant when adjusting for disease severity, measured by the PUCAI index (P= 0.32, PUCAI score P<0.001). In accordance, IL-6 was highly correlated with C-reactive protein (r= 0.41, P<0.001) and albumin (r= -0.64, P<0.001). Reflecting internal validity of the assay, GBA was highly correlated with the last corticosteroid dose and the time interval to bloodletting (r= -0.41 and r= -0.54, respectively; both P<0.001). There was no statistically significant difference in the GBA levels between responders and non-responders (249nM · versus 200nM · cortisol equivalent, P= 0.18). In a multivariate regression model adjusted for time elapsed from corticosteroids and the administered dose, GBA did not predict response or refractoriness to corticosteroids (P= 0.34). Disease severity is associated with response to steroid therapy, while steroid bioavailability, steroid dosing, and the type of inflammation are not. IL-6 is a strong predicting variable for refractoriness, but it merely reflects disease severity. Nonetheless, IL-6 levels may have a role in predicting response to steroids in pediatric UC, thereby influencing treatment decision making.

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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 score0.002
Threshold uncertainty score0.782

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 head0.265
Teacher spread0.256 · 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