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Record W2156614790

Emergence of influenza: expecting the unexpected: 2013 Reginald Thomson Lecture.

2013· article· en· W2156614790 on OpenAlexaboutno aff
Thijs Kuiken

Bibliographic record

VenuePubMed · 2013
Typearticle
Languageen
FieldMedicine
TopicZoonotic diseases and public health
Canadian institutionsnot available
Fundersnot available
KeywordsEconomic shortageVeterinary medicinePopulationMedicineGovernment (linguistics)
DOInot available

Abstract

fetched live from OpenAlex

Dr. Reginald G. Thomson was the founding dean of the Atlantic Veterinary College and had an impressive career (1). After studying veterinary medicine at the Ontario Veterinary College, he obtained his PhD at Cornell University and in the same year, 1965, became a diplomate of the American College of Veterinary Pathologists. Dr. Thomson was a distinguished researcher and prolific writer. He published more than 60 research papers, mainly on pathology of respiratory disease, was editor of the Canadian Journal of Comparative Medicine (now Canadian Journal of Veterinary Research), and wrote two textbooks, “General Veterinary Pathology” and “Special Veterinary Pathology.” The latter formed the basis of the current textbook “Pathologic Basis of Veterinary Disease” (2). Furthermore, Dr. Thomson had an international vision for veterinary medicine. This is demonstrated by his links with universities in Kenya, Nigeria, and Iraq, his sabbatical leaves spent in Africa and Asia, as well as the rotations on international veterinary medicine and foreign animal diseases he helped establish at the Atlantic Veterinary College (1). Such an international vision is necessary to meet the current challenges to our society, in which the growth of the global human population on one hand, and the growth in average consumption per person on the other, have many far-reaching effects, such as climate change, water shortage, deforestation, depletion of fish stocks (3), and an increased rate of emergence and re-emergence of infectious diseases (4). The subject of this report is about just one of these emerging diseases, influenza. In this lecture, I have two objectives: one is to inform you of new and unexpected aspects of the versatile micro-organism, influenza A virus; the other is to show how veterinary medicine, and specifically veterinary pathology, can make important contributions to knowledge of diseases not only in domestic animals, but also in humans and wildlife. Influenza A virus is a virus species belonging to the family Orthomyxoviridae (5). It is an enveloped, negative-strand RNA virus with a genome consisting of 8 segments. The segmented character of the genome allows reassortment, that is, if 2 viruses simultaneously infect the same cell, progeny viruses may be produced that have gene segments from both parent viruses. The envelope of the virus contains two surface antigens: the hemagglutinin, which is important for attachment of the virus to its target cell, and the neuraminidase, which is an enzyme that allows progeny viruses to be released from the surface of the cell that generated them. Influenza A viruses are categorized based on the subtype of their hemagglutinin (H1 to H17) and of their neuraminidase (N1 to N10). The original reservoir for all influenza A viruses, with the exception of H17N10, originating from a bat species (6), are wild waterbirds (7). From this reservoir, influenza A viruses occasionally jump to other species, both domestic birds and mammals. Usually, this leads to individual cases of infection or short-lived epidemics, such as in harbor seals (Phoca vitulina). Rarely, the virus can maintain itself in the new host species, as is the case in domestic pigs, horses, and humans (8). In the last hundred years, such events occurred four times in the human population: H1N1 in 1918, H2N2 in 1957, H3N2 in 1968, and H1N1 in 2009 (9).

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)0.006
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.010
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0100.008

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 head0.035
GPT teacher head0.271
Teacher spread0.236 · 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
GenreCommentary

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
Published2013
Admission routes1
Has abstractyes

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