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Record W2610656337 · doi:10.1093/cid/cix123

From SARS to Avian Influenza Preparedness in Hong Kong

2017· article· en· W2610656337 on OpenAlexaff
Andrew Tin Yau Wong, Hong Chen, Shao‐Haei Liu, Enoch Hsu, Kristine Shik Luk, Christopher K.C. Lai, Regina Chan, O. T. Y. Tsang, Kitty Wai Ying Choi, Mike Yat Wah Kwan, Anna Tong, Vincent Chi‐Chung Cheng, Dominic N.C. Tsang

Bibliographic record

VenueClinical Infectious Diseases · 2017
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsPreparednessMedicineInfluenza A virus subtype H5N1PandemicPublic healthInfection controlGovernment (linguistics)Medical emergencyEpidemiologyEmergency managementEnvironmental healthCoronavirus disease 2019 (COVID-19)Infectious disease (medical specialty)ImmunologyNursingIntensive care medicineDiseaseEconomic growthPolitical sciencePathology

Abstract

fetched live from OpenAlex

The first human H5N1 case was diagnosed in Hong Kong in 1997. Since then, experience in effective preparedness strategies that target novel influenza viruses has expanded. Here, we report on avian influenza preparedness in public hospitals in Hong Kong to illustrate policies and practices associated with control of emerging infectious diseases. The Hong Kong government's risk-based preparedness plan for influenza pandemics includes 3 response levels for command, control, and coordination frameworks for territory-wide responses. The tiered levels of alert, serious, and emergency response enable early detection based on epidemiological exposure followed by initiation of a care bundle. Information technology, laboratory preparedness, clinical and public health management, and infection control preparedness provide a comprehensive and generalizable preparedness plan for emerging infectious diseases.

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)0.001
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 score0.316
Threshold uncertainty score0.629

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.191
GPT teacher head0.518
Teacher spread0.327 · 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

Citations44
Published2017
Admission routes1
Has abstractyes

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