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Record W4311666656 · doi:10.1515/jpem-2022-0444

Diagnosis of adrenal insufficiency in children: a survey among pediatric endocrinologists in North America

2022· article· en· W4311666656 on OpenAlexaff
Carolina Silva, Trisha Patel, Carol Lam

Bibliographic record

VenueJournal of Pediatric Endocrinology and Metabolism · 2022
Typearticle
Languageen
FieldMedicine
TopicAdrenal Hormones and Disorders
Canadian institutionsSickKids FoundationHospital for Sick ChildrenBC Children's HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineAdrenal insufficiencyPediatricsPediatric endocrinologyEndocrinology

Abstract

fetched live from OpenAlex

OBJECTIVES: Adrenal insufficiency (AI) is a life-threatening condition where an accurate diagnosis is critical. While the ACTH stimulation test is the diagnostic test of choice, there remains uncertainty around its protocols and interpretation of results. In this context, the objective of this study was to understand practices of North American pediatric endocrinology providers on the diagnosis of AI in children. METHODS: An anonymous electronic survey was sent to members of the Pediatric Endocrine Society. RESULTS: A total of 221 participants were included. The majority practiced in academic centers (78%). All respondents ordered ACTH stimulation tests. While 85% used high-dose ACTH stimulation tests (HDST) to diagnose primary AI, there was less consistency in the choice of tests (HDST vs. low-dose ACTH stimulation test; LDST) when diagnosing secondary AI. When interpreting results, 95% used peak cortisol levels, 70% considered the clinical picture, and 49% used relative increase in cortisol levels. Median (IQR) cortisol cutoff level after ACTH stimulation test that was considered sufficient was 18 (15.5-18) μg/L [496 (428-496) nmol/L]; 17% used different cutoffs for LDST, and 18% used different cutoffs for newborns. Finally, 47% were unaware of the assay that was used in their institution for cortisol measurements. CONCLUSIONS: Pediatric endocrinology providers use ACTH stimulation tests variably, including in the choice between HDST vs. LDST, test protocols, and interpretation of results.

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)0.001
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 score0.004
Threshold uncertainty score0.834

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.252
Teacher spread0.239 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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