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Record W4389815885 · doi:10.1371/journal.pone.0295909

Epidemiology and clinical manifestations of reported Lyme disease cases: Data from the Canadian Lyme disease enhanced surveillance system

2023· article· en· W4389815885 on OpenAlexaffabout
Kiera R. Murison, Christy H. Wilson, Katie M. Clow, Salima Gasmi, Todd F. Hatchette, Annie‐Claude Bourgeois, Gerald A. Evans, Jules K. Koffi

Bibliographic record

VenuePLoS ONE · 2023
Typearticle
Languageen
FieldImmunology and Microbiology
TopicVector-borne infectious diseases
Canadian institutionsQueen's UniversityPublic Health Agency of CanadaNova Scotia Health AuthorityDalhousie UniversityKingston Health Sciences CentreUniversity of Guelph
Fundersnot available
KeywordsLyme diseaseEpidemiologyMedicineDiseaseIncidence (geometry)LYMEPediatricsDisease surveillanceBorrelia burgdorferiInternal medicineImmunology

Abstract

fetched live from OpenAlex

Lyme disease cases reported in seven Canadian provinces from 2009 to 2019 through the Lyme Disease Enhanced Surveillance System are described herein by demographic, geography, time and season. The proportion of males was greater than females. Bimodal peaks in incidence were observed in children and older adults (≥60 years of age) for all clinical signs except cardiac manifestations, which were more evenly distributed across age groups. Proportions of disease stages varied between provinces: Atlantic provinces reported mainly early Lyme disease, while Ontario reported equal proportions of early and late-stage Lyme disease. Early Lyme disease cases were mainly reported between May through November, whereas late Lyme disease were reported in December through April. Increased awareness over time may have contributed to a decrease in the proportion of cases reporting late disseminated Lyme disease. These analyses help better describe clinical features of reported Lyme disease cases in Canada.

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)0.003
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 score0.019
Threshold uncertainty score0.138

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.007
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.147
GPT teacher head0.333
Teacher spread0.186 · 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

Citations7
Published2023
Admission routes2
Has abstractyes

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Same venuePLoS ONESame topicVector-borne infectious diseasesFrench-language works237,207