MétaCan
Menu
Back to cohort
Record W2135147363 · doi:10.1186/s13223-015-0075-z

CSACI position statement: systemic effect of inhaled corticosteroids on adrenal suppression in the management of pediatric asthma

2015· article· en· W2135147363 on OpenAlexaffvenueabout
Karine Issa-El-Khoury, Harold Kim, Edmond S. Chan, Tim Vander Leek, Francisco Noya

Bibliographic record

VenueAllergy Asthma and Clinical Immunology · 2015
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsUniversity of AlbertaMcMaster UniversityWestern UniversityUniversity of British ColumbiaMcGill University
Fundersnot available
KeywordsMedicineAsthmaInhaled corticosteroidsCorticosteroidAllergyMorningDiseasePediatricsIntensive care medicineInternal medicineImmunology

Abstract

fetched live from OpenAlex

Asthma is a chronic inflammatory disease of the airways that affects a growing number of children and adolescents. Inhaled corticosteroids (ICS) are the mainstay of treatment in persistent asthma, with a stepwise approach to increasing doses of ICS depending on asthma severity and control. ICS have known local and systemic side effects, of which adrenal suppression is still under-recognized. The latter is associated with chronic exposure and higher doses, although it has rarely been reported in children receiving low doses for a short period of time. The Canadian Society of Allergy and Clinical Immunology (CSACI) therefore recommends that physicians screen for adrenal suppression in children receiving high doses for more than 6 months and to consider screening those on medium dose if the risk is deemed higher by factors that increase an individual's systemic corticosteroid exposure. Morning serum cortisol level can be used as a screening tool and abnormal results or normal results with a high index of suspicion should be confirmed with low-dose ACTH stimulation tests.

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)0.007
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.013
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.002
Science and technology studies0.0020.002
Scholarly communication0.0040.002
Open science0.0040.002
Research integrity0.0220.025
Insufficient payload (model declined to judge)0.0060.007

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 head0.021
GPT teacher head0.328
Teacher spread0.307 · 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 designNot applicable
Domainnot available
GenreEditorial

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

Citations39
Published2015
Admission routes3
Has abstractyes

Explore more

Same venueAllergy Asthma and Clinical ImmunologySame topicAsthma and respiratory diseasesFrench-language works237,207