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Record W3154336605

Associations between the Spatiotemporal Distribution of Kawasaki Disease and Environmental Factors: Discovering Clues into the Elusive Etiology of a Complex Disease

2020· dissertation· en· W3154336605 on OpenAlexaboutno aff
Tisiana Low

Bibliographic record

VenueTSpace · 2020
Typedissertation
Languageen
FieldMedicine
TopicKawasaki Disease and Coronary Complications
Canadian institutionsnot available
Fundersnot available
KeywordsDiseaseEtiologyKawasaki diseaseDistribution (mathematics)Complex diseaseGeographyData scienceMedicineComputer sciencePathologyInternal medicineMathematics
DOInot available

Abstract

fetched live from OpenAlex

Kawasaki disease is an acute vasculitis of childhood which may lead to coronary artery aneurysms, and is the most common cause of acquired heart disease in children in developed countries. The etiology remains unknown, but is currently felt to be the result of a dysregulation and hyper-stimulation of the inflammatory system occurring in genetically and developmentally susceptible individuals after exposure to one or more unidentified infectious or environmental trigger(s). The aim of this thesis was to determine how variations in the incidence of KD in Canada are spatiotemporally associated with the incidence of infections and atopic conditions, wind and weather components, pollution and aeroallergens. We established that immunomodulation may be influenced by pollution and habitual exposure to aeroallergens. We proposed that fungal and algal spores, viral gastrointestinal and bacterial lower respiratory tract infections may trigger KD. These findings add to the etiologic framework for KD.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.666

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 integrity0.0000.000
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 head0.029
GPT teacher head0.330
Teacher spread0.301 · 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.

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
Published2020
Admission routes1
Has abstractyes

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