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Record W2596510259 · doi:10.12688/f1000research.9561.1

Recent advances in Japanese encephalitis

2017· preprint· en· W2596510259 on OpenAlexaff
Anirban Basu, Kallol Dutta

Bibliographic record

VenueF1000Research · 2017
Typepreprint
Languageen
FieldMedicine
TopicMosquito-borne diseases and control
Canadian institutionsInstitut Universitaire en Santé Mentale de Québec
Fundersnot available
KeywordsDiseaseJapanese encephalitisMedicineSoutheast asiaVaccinationEncephalitisPopulationVirologyPathologyEnvironmental healthHistoryVirusEthnology

Abstract

fetched live from OpenAlex

Japanese encephalitis is a flaviviral disease that is endemic to the South, Southeast Asia, and Asia Oceania regions. Given that about 60% of the world's population (about 7.4 billion) resides in this region (about 4.4 billion), this disease poses a significant threat to global health. Active vaccination campaigns conducted in endemic countries have led to a decrease in the number of reported cases over the years. In this article, we strive to briefly highlight recent advances in understanding the role of microRNAs in disease pathology, focus on providing brief summaries of recent clinical trials in the field of Japanese encephalitis therapeutics, and review the current prophylactic strategies.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.003

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 head0.049
GPT teacher head0.411
Teacher spread0.362 · 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 designNot applicable
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

Citations16
Published2017
Admission routes1
Has abstractyes

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