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Vitamin use among children attending a Canadian pediatric emergency department

2010· article· en· W1709292845 on OpenAlexaffabout
Ran D. Goldman, Sunita Vohra, Alexander L. Rogovik

Bibliographic record

VenueFundamental and Clinical Pharmacology · 2010
Typearticle
Languageen
FieldMedicine
TopicComplementary and Alternative Medicine Studies
Canadian institutionsSt. Michael's HospitalHospital for Sick ChildrenUniversity of British ColumbiaStollery Children's HospitalUniversity of AlbertaChild and Family Research InstituteBC Children's Hospital
Fundersnot available
KeywordsEmergency departmentMedicineVitaminPediatricsMedical emergencyEmergency medicinePsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Increasing use of vitamins has been documented worldwide in children and adolescents, and potential for vitamin-drug interactions exists. The aim of this study was to identify vitamin use by children visiting a pediatric emergency department (ED). A survey of parents and/or patients 0-18 years was conducted at a large pediatric ED in Canada. A total of 1804 families were interviewed. The main outcome measure was prevalence of vitamin use by children in the preceding 3 months. A third (32.3%) of the patients in our cohort had used vitamins in the preceding 3 months, and 48% of them were taking vitamins daily. Over 8% of all children used vitamins within the last 24 h. The use of vitamins was higher with older patient and parental age (P<0.001), chronic patient illness (P<0.001), completed immunization (P<0.001), concurrent patient use of prescribed medications (P=0.02), higher parental education (P<0.01), and English as a primary language spoken at home (P=0.002). Prevalence of vitamin use among children in the ED is 32% in the preceding 3 months and 8% within the last 24 h. In light of these findings, pediatricians should ask about vitamin use and discuss with parents potential interactions and possible adverse effects.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.996

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.045
GPT teacher head0.395
Teacher spread0.350 · 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.

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

Citations4
Published2010
Admission routes2
Has abstractyes

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