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Record W2133400164 · doi:10.1097/ccm.0b013e3181cbaf34

Influenza epidemiology—past, present, and future

2010· review· en· W2133400164 on OpenAlexaffabout
Philippe Lagacé‐Wiens, Ethan Rubinstein, Abba B. Gumel

Bibliographic record

VenueCritical Care Medicine · 2010
Typereview
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPandemicEpidemiologyMedicineHuman mortality from H5N1Influenza pandemicDiseaseIntensive careTransmission (telecommunications)PopulationIntensive care medicineIncidence (geometry)Influenza A virusVirologyEnvironmental healthVirusInfectious disease (medical specialty)Coronavirus disease 2019 (COVID-19)Pathology

Abstract

fetched live from OpenAlex

In April 2009, Mexican, American, and Canadian authorities announced that a novel influenza virus with pandemic potential had been identified in large segments of the population. Within weeks, it became apparent that the world was dealing with the first influenza pandemic in >40 yrs. Despite the unpredictable nature of influenza severity and spread in the pandemics of the 20th century, understanding the epidemiology of the past pandemics and current influenza pandemic will help prepare physicians, hospitals, and governments to predict and prepare for the subsequent waves and subsequent pandemics. We present a summary of the biology that predisposes influenza to cause sudden pandemics, as well as a summary of the epidemiology of the 20th century pandemics. We also report on the epidemiology, disease severity, and risk factors for severe disease and intensive care admission from the first wave of the current pandemic (April-August 2009). Last, we provide a mathematical model based on transmission dynamics of the H1N1 influenza virus that may provide some guidance in terms of disease incidence and hospital impact.

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)0.001
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.955
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.000
Bibliometrics0.0000.000
Science and technology studies0.0000.004
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.004
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 head0.256
GPT teacher head0.540
Teacher spread0.283 · 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; both teacher heads agree on what is shown here.

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

Citations83
Published2010
Admission routes2
Has abstractyes

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