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Risk Assessment of Enterovirus D68 in Taiwan

2015· article· en· W2417227586 on OpenAlexaboutno aff
Shu-Wan Jian, Chia-Lin Lee, Ding‐Ping Liu

Bibliographic record

VenueEpidemiology bulletin · 2015
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Immunology Research
Canadian institutionsnot available
Fundersnot available
KeywordsOutbreakEpidemiologyMedicineEnterovirusEnvironmental healthPopulationMyelitisPediatricsMedical emergencyVirologyVirusSpinal cordPathology

Abstract

fetched live from OpenAlex

Enterovirus D68 (EV-D68) was associated with a widespread outbreak of severe respiratory disease since August 2014 in the US and acute flaccid myelitis during the 2014 outbreak. Some regions in Canada and European countries have also reported sporadic cases of laboratory-confirmed EV-D68 infections. Frequent travelling between the US, Europe and Taiwan posed a threat to Taiwan during enterovirus season, which triggered the risk assessment on EV-D68. We estimated the probability and population-level impact of EV-D68 infections in Taiwan by referring to the risk assessment reports on EV-D68, the international risk assessment framework and algorithm, viral characteristics, global epidemiology, susceptibility of the population and other information currently available. To date, the available information and scientific evidence indicated that the likelihood of any sporadic cases and clusters due to EV-D68 in Taiwan is medium, while the risk that sporadic case of severe respiratory illness and acute flaccid myelitis detected is due to EV-D68 is very low. EV-D68 detection and surveillance in Taiwan should be considered and enhanced in persons with severe unexplained acute respiratory disease or unexplained neurological symptoms. We recommended that healthcare professionals should be vigilant about preventing the spread of EV-D68, perform wash hands correctly and maintain a hygienic environment during enterovirus seasons.

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)0.005
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 score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.104
GPT teacher head0.428
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

Citations0
Published2015
Admission routes1
Has abstractyes

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