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Record W4307054686 · doi:10.1093/pch/pxac100.010

11 Clinician Management of Childhood Dyslipidemia in the Community Setting

2022· article· en· W4307054686 on OpenAlexaffabout
Katherine N. Tom, Natasha De Silva, Alicia Polack, Karishma Singh, Charles Keown‐Stoneman, Jonathon L. Maguire, Catherine S. Birken, Peter J. Wong

Bibliographic record

VenuePaediatrics & Child Health · 2022
Typearticle
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsHospital for Sick ChildrenSickKids FoundationWestern UniversityUniversity of TorontoMcMaster University
Fundersnot available
KeywordsDyslipidemiaMedicineFamily historyDiseaseDiabetes mellitusPediatricsStroke (engine)GerontologyFamily medicineInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

Abstract Background Childhood dyslipidemia is a known risk factor for the development of cardiovascular disease (CVD) in adulthood. Although adverse health outcomes of dyslipidemias are rare in childhood, the atherosclerotic process begins in early life. An overlooked lifelong progression of disease may result in myocardial infarction and stroke in later life. There are currently no Canadian paediatric guidelines for lipid screening. Despite dyslipidemia identification, early treatment or management may not be initiated. Primary care providers (PCP) are well positioned to advise and reinforce cardiovascular health behaviours to minimize the risk of CVD and promote lifelong cardiovascular health. Objectives To describe clinician practice patterns associated with childhood dyslipidemia management in the community setting. Design/Methods A retrospective chart review was conducted for children 2 to 10 years of age with abnormal lipid profiles. Participants were recruited from a practice-based research network. Non-fasting blood samples were obtained. The primary study outcome was the proportion of physicians engaging in each step of management practice. R version 3.6.2 (R Foundation for Statistical Computing, Vienna, Austria) was used for statistical analysis. Results Among 462 children identified with dyslipidemia, all were seen by PCP at their next follow-up visit. PCP rarely informed families about abnormal lipid profiles. PCP frequently counselled on diet and eating habits (n=424, 95.1%), but less often on physical activity (n=154, 34.5%), screen time (n=24, 5.4%), and sleep (n=1, 0.2%). Family history of CVD, diabetes, high cholesterol, or hypertension was infrequently discussed (n=5, 1.1%). PCP repeated fasting lipid profiles uncommonly (n=20, 4.5%). Management plans for abnormal lipid profiles were not documented. Only one participant had a follow-up visit (n=1, 0.2%). Referrals were rarely made to dieticians (n=2, 0.4%) and were not in response to abnormal lipid levels. Conclusion Dyslipidemia in childhood is a risk factor for the development of adult cardiovascular disease. Among children with abnormal lipid profiles, our study showed PCP rarely identified and initiated early management for abnormal lipid levels. Our results may inform the need for paediatric lipid screening and management guidelines to develop best clinical practice.

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.020
GPT teacher head0.298
Teacher spread0.278 · 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 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

Citations1
Published2022
Admission routes2
Has abstractyes

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