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Record W2530334099 · doi:10.1002/acr.23123

Frequency and Duration of Adrenal Suppression Following Glucocorticoid Therapy in Children With Rheumatic Diseases

2016· article· en· W2530334099 on OpenAlexaff
Alexandra Ahmet, Vincent Brienza, Audrey Tran, Julie Lemieux, Mary Aglipay, Nick Barrowman, Ciarán M. Duffy, Johannes Roth, Roman Juřenčák

Bibliographic record

VenueArthritis Care & Research · 2016
Typearticle
Languageen
FieldMedicine
TopicAdrenal Hormones and Disorders
Canadian institutionsQueen's UniversityUniversity of TorontoUniversity of Ottawa
Fundersnot available
KeywordsMedicineDiscontinuationGlucocorticoidAdrenocorticotropic hormoneProspective cohort studyMorningCohortAdrenal crisisAdrenal insufficiencyInternal medicinePediatricsHormone

Abstract

fetched live from OpenAlex

OBJECTIVE: Adrenal suppression (AS), a glucocorticoid (GC) side effect with potentially significant morbidity, is poorly understood. The purpose of our study was to determine frequency, duration, and predictors of AS following a gradual taper of GC in children with rheumatic conditions. METHODS: A prospective, observational cohort study was conducted. All patients ages ≤16 years ready to discontinue GC after >4 weeks of therapy were included. Morning cortisol was tested 4 weeks after GC taper to physiologic doses and then repeatedly until normalization. GCs were subsequently discontinued and a low-dose adrenocorticotropic hormone stimulation test was performed. RESULTS: The study was completed by 31 of 39 patients. The median age was 12.9 years and median duration of GC therapy was 39.6 weeks. Seventeen patients (54.8%) had AS. Of the patients with AS, 50% showed recovery by 7 months. Two patients had persistent AS at 12 months and 1 patient at 2 years. A higher maximum GC dose was a significant predictor for the development of AS. CONCLUSION: More than 50% of our patients had AS after GC discontinuation, despite a gradual taper of GC. Stress steroids should be considered in children treated with long-term GC, even after steroid discontinuation, to prevent possible adrenal crisis.

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 score0.261
Threshold uncertainty score0.223

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 head0.015
GPT teacher head0.308
Teacher spread0.293 · 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

Citations22
Published2016
Admission routes1
Has abstractyes

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