MétaCan
Menu
Back to cohort
Record W2109262816 · doi:10.1586/14760584.6.2.169

Immunization strategies for the prevention of pneumovirus infections

2007· review· en· W2109262816 on OpenAlexaff
Nicholas J. Bennett, John Ellis, Cynthia A. Bonville, Helene F. Rosenberg, Joseph B. Domachowske

Bibliographic record

VenueExpert Review of Vaccines · 2007
Typereview
Languageen
FieldMedicine
TopicRespiratory viral infections research
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsPneumovirusBronchiolitisVirologyParamyxoviridaePneumoniaVirusMononegaviralesPneumovirinaeBiologyHuman Parainfluenza VirusImmunizationRespiratory tract infectionsImmunologyHuman metapneumovirusViral diseaseRespiratory diseaseHuman pathogenPathogenMedicineRespiratory systemAntigenBacteriaLung

Abstract

fetched live from OpenAlex

Pneumoviruses, which are viruses of the family Paramyxoviridae, subfamily Pneumovirinae, are pathogens that infect the respiratory tract of their host species. The human pneumovirus pathogen, human respiratory syncytial virus (RSV), has counterparts that infect cows (bovine RSV), sheep (ovine RSV), goats (caprine RSV) and rodents (pneumonia virus of mice). Each pneumovirus is host specific and results in a spectrum of disease, ranging from mild upper-respiratory illness to severe bronchiolitis and pneumonia with significant morbidity and mortality. Given the public health burden caused by human RSV and the concomitant agricultural impact of bovine RSV, these two viruses are considered as prime targets for the development of safe and effective vaccines. In this review, we describe the strategies used to develop vaccines against human and bovine RSV and introduce the pneumonia virus mouse model as a novel and invaluable tool for preclinical studies and new vaccine 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)0.001
metaresearch head score (Gemma)0.000
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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.154
GPT teacher head0.519
Teacher spread0.365 · 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

Citations9
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueExpert Review of VaccinesSame topicRespiratory viral infections researchFrench-language works237,207