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Record W2278764476 · doi:10.1177/2333794x16630670

Incidence Rate and Epidemiological and Clinical Aspects of Kawasaki Disease in Children of Maghrebi Origin in the Province of Quebec, Canada, Compared to the Country of Origin

2016· article· en· W2278764476 on OpenAlexaffabout
Arbia Abir Gorrab, Anne Fournier, Asma Bouaziz, Linda Spigelblatt, Rosie Scuccimarri, Ali Mrabet, Nagib Dahdah

Bibliographic record

VenueGlobal Pediatric Health · 2016
Typearticle
Languageen
FieldMedicine
TopicKawasaki Disease and Coronary Complications
Canadian institutionsMcGill UniversityMontreal Children's HospitalHôpital Maisonneuve-RosemontUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsKawasaki diseaseMedicineIncidence (geometry)EpidemiologyDiseaseDemographyPediatricsPublic healthRetrospective cohort studySurgeryInternal medicinePathology

Abstract

fetched live from OpenAlex

The incidence of Kawasaki disease in Maghreb countries is apparently low, unlike those living in the province of Quebec, Canada. This retrospective study compared Maghrebi children living in Quebec to the countries of origin, Morocco, Algeria, and Tunisia. The annualized incidence rate in Quebec (18.49/year/100 000 children under 5 years of age) was 4 to 12 times higher than in Tunisia, Morocco, and Algeria (0.95, 4.52, and 3.15, respectively). The prevalence of incomplete diagnostic criteria was higher in Quebec at 39%, Morocco 43%, and Tunisia 39% compared to Algeria at 8%, with minimal delayed diagnosis (7%) only in Quebec compared to 30%, 35%, and 62%, respectively (P < .001). The rate of coronary aneurysms was comparable however (11% in Quebec vs 4%, 10%, and 25%, in Tunisia, Morocco, and Algeria, respectively; P = .31). The higher incidence of Kawasaki disease in the Maghreb community in Quebec versus the countries of origin seems due to underdiagnosis, which represents a public health concern in those countries.

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)0.000
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 score0.053
Threshold uncertainty score0.106

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 studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.027
GPT teacher head0.350
Teacher spread0.323 · 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

Citations14
Published2016
Admission routes2
Has abstractyes

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