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Dyslipidemia in Children With Arterial Ischemic Stroke: Prevalence and Risk Factors

2017· article· en· W2761215554 on OpenAlexaff
Sally Sultan, Michael M. Dowling, Adam Kirton, Gabrielle deVeber, Alexandra Linds, Mitchell S.V. Elkind, Tim Bernard, Marta Hernández, Michael Rivkin, Ilona Kopyta, Rebecca Ichord, Susan Benedict, Mark T. Mackay, Dimitrios Zafeiriou, M. Troncoso, Jerome Y. Yager, Lisa Abraham, Warren Lo, Verónica González, Montri Saengpattrachai, Anthony K.C. Chan, Abdallah Abdallah, Vesna Branković-Srećković, Anneli Kolk, Jessica L. Carpenter, Gordana Kovačević, Catherine Amlie‐Lefond, Maja Steinlin, Juliann Paolicchi, Monroe Carell, Bruce Björnson, Barry E. Kosofsky, Virginia Wong, Paola Pergami, Neil Friedman, Guang Yang, Peter Humphreys, Ulrike Nowak‐Göttl, Donna M. Ferriero, Frederico Xavier, R. H. Fryer, Lucila Andrade Alveal, Diana Altuna, Ryan J. Felling, Steven G. Pavlakis, Eríc F. Grabowski, Meredith R. Golomb, Michael J. Noetzel, Chaouki Khoury, Norma B. Lerner, A. D. Blair, Mubeen F. Rafay

Bibliographic record

VenuePediatric Neurology · 2017
Typearticle
Languageen
FieldMedicine
TopicBlood Coagulation and Thrombosis Mechanisms
Canadian institutionsHospital for Sick ChildrenUniversity of CalgarySickKids FoundationAlberta Children's Hospital
FundersNational Institute of Neurological Disorders and StrokeNational Institute of General Medical SciencesNational Heart, Lung, and Blood Institute
KeywordsMedicineDyslipidemiaHypertriglyceridemiaBody mass indexStroke (engine)OverweightInternal medicinePopulationPediatricsCholesterolObesityTriglycerideEnvironmental health

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.251
Teacher spread0.238 · 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

Citations30
Published2017
Admission routes1
Has abstractno

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