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Record W2902371416 · doi:10.1017/s0950268818003096

La Crosse virus: a scoping review of the global evidence

2018· review· en· W2902371416 on OpenAlexafffundabout
Shannon Harding, Judy Greig, Mariola Mascarenhas, Ian Young, Lisa Waddell

Bibliographic record

VenueEpidemiology and Infection · 2018
Typereview
Languageen
FieldMedicine
TopicViral Infections and Vectors
Canadian institutionsToronto Metropolitan UniversityPublic Health Agency of Canada
FundersPublic Health AgencyPublic Health Agency of Canada
KeywordsVirologyMedicine

Abstract

fetched live from OpenAlex

Urbanisation and climate change are altering the pattern of California serogroup viruses in North America. As La Crosse virus (LACV) is the most pathogenic of the California serogroup, it is important to identify changes in distribution, transmission and pathogenesis. A scoping review (ScR) was prioritised to summarise the global evidence on LACV. A comprehensive search strategy was used, identified references were screened for relevance and relevant articles were characterised. Each step was conducted by two independent reviewers using pre-tested forms. Analysis identified areas of research saturation and gaps. The ScR included 481 research articles that were mostly journal articles (78.2%) conducted in North America (90.9%) from 1969 to 2016. Most evidence focused on epidemiology (44.9%), virus characteristics (25.8%), transmission conditions (18.7%) and pathogenesis of LACV in hosts (18.3%). Fewer studies evaluated the accuracy of diagnostic tests (8.7%), the efficacy of treatments (3.5%), prevention and control strategies (3.1%), the economic burden of infection (0.6%) and social impact (0.2%) of LACV. None of the literature predicted the impact of climate change on LACV, nor were any cases reported in Canada. These findings are intended to guide research to close knowledge gaps and inform evidence-based decisions surrounding activities for the prevention and control of LACV.

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)0.003
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.414
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.217
GPT teacher head0.508
Teacher spread0.291 · 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 designSystematic review
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

Citations50
Published2018
Admission routes3
Has abstractyes

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