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Record W4308202928 · doi:10.1016/j.molmed.2022.10.001

Adenoviruses in medicine: innocuous pathogen, predator, or partner

2022· review· en· W4308202928 on OpenAlexafffund
Katelyn M. MacNeil, Mackenzie J. Dodge, Andris M. Evans, Tanner M. Tessier, Jason B. Weinberg, Joe S. Mymryk

Bibliographic record

VenueTrends in Molecular Medicine · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsLawson Health Research InstituteWestern University
FundersNatural Sciences and Engineering Research Council of CanadaNational Institute of Allergy and Infectious DiseasesCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsPathogenBiologyVirologyPredatorComputational biologyMicrobiologyEcologyPredation

Abstract

fetched live from OpenAlex

The consequences of human adenovirus (HAdV) infections are generally mild. However, despite the perception that HAdVs are harmless, infections can cause severe disease in certain individuals, including newborns, the immunocompromised, and those with pre-existing conditions, including respiratory or cardiac disease. In addition, HAdV outbreaks remain relatively common events and the recent emergence of more pathogenic genomic variants of various genotypes has been well documented. Coupled with evidence of zoonotic transmission, interspecies recombination, and the lack of approved AdV antivirals or widely available vaccines, HAdVs remain a threat to public health. At the same time, the detailed understanding of AdV biology garnered over nearly 7 decades of study has made this group of viruses a molecular workhorse for vaccine and gene therapy applications.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.004

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 head0.430
Teacher spread0.326 · 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

Citations78
Published2022
Admission routes2
Has abstractyes

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