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Record W2187743274 · doi:10.1186/s40985-015-0011-2

Screening for cardiovascular disease risk factors beginning in childhood

2015· review· en· W2187743274 on OpenAlexaff
Clemens Bloetzer, Pascal Bovet, Joan-Carles Surı́s, Umberto Siméoni, Gilles Paradis, Arnaud Chioléro

Bibliographic record

VenuePublic health reviews · 2015
Typereview
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsMcGill University Health CentreMcGill University
Fundersnot available
KeywordsMedicineDiseaseIntervention (counseling)Blood pressureRisk factorDiabetes mellitusIntensive care medicineEnvironmental healthPediatricsInternal medicinePsychiatryEndocrinology

Abstract

fetched live from OpenAlex

Cardiovascular diseases (CVD) are the leading cause of death worldwide. Individual detection and intervention on CVD risk factors and behaviors throughout childhood and adolescence has been advocated as a strategy to reduce CVD risk in adulthood. The U.S. National Heart, Lung, and Blood Institute (NHLBI) has recently recommended universal screening of several risk factors in children and adolescents, at odds with several recommendations of the U.S. Services Task Force and of the U.K. National Screening committee. In the current review, we discuss the goals of screening for CVD risk factors (elevated blood pressure, abnormal blood lipids, diabetes) and behaviors (smoking) in children and appraise critically various screening recommendations. Our review suggests that there is no compelling evidence to recommend universal screening for elevated blood pressure, abnormal blood lipids, abnormal blood glucose, or smoking in children and adolescents. Targeted screening of these risk factors could be useful but specific screening strategies have to be evaluated. Research is needed to identify target populations, screening frequency, intervention, and follow-up. Meanwhile, efforts should rather focus on the primordial prevention of CVD risk factors and at maintaining a lifelong ideal cardiovascular health through environmental, policy, and educational approaches.

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)0.010
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.965
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0100.005
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.254
GPT teacher head0.417
Teacher spread0.164 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations49
Published2015
Admission routes1
Has abstractyes

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