MétaCan
Menu
Back to cohort
Record W2132990407 · doi:10.1186/1742-4933-9-23

CMV and Immunosenescence: from basics to clinics

2012· article· en· W2132990407 on OpenAlexaff
Rafael Solana, Raquel Tarazona, Allison E. Aiello, Arne N. Akbar, Victor Appay, Mark Beswick, Jos A. Bosch, Carmen Campos, Sara Cantisán, Luka Čičin‐Šain, Evelyna Derhovanessian, Sara Ferrando‐Martínez, Daniela Frasca, Tamàs Fülöp, Sheila Govind, Beatrix Grubeck‐Loebenstein, Ann Hill, Mikko Hurme, Florian Kern, Anis Larbi, Miguel López‐Botet, Andrea B. Maier, Janet E. McElhaney, Paul Moss, Elissaveta Naumova, Janko Nikolich‐Žugich, Alejandra Pera, Jerrald L. Rector, Natalie E. Riddell, Beatriz Sánchez-Correa, Paolo Sansoni, Delphine Sauce, René A. W. van Lier, George C. Wang, Mark R. Wills, Maciej Zieliński, Graham Pawelec

Bibliographic record

VenueImmunity & Ageing · 2012
Typearticle
Languageen
FieldMedicine
TopicCytomegalovirus and herpesvirus research
Canadian institutionsUniversity of British ColumbiaUniversité de Sherbrooke
FundersNational Institute of Allergy and Infectious DiseasesNational Institute on AgingBiotechnology and Biological Sciences Research CouncilUniversidad de Córdoba
KeywordsImmunosenescenceCytomegalovirusImmunologyImmunityMedicineImmune systemVirologyBiologyVirusHerpesviridaeViral disease

Abstract

fetched live from OpenAlex

Alone among herpesviruses, persistent Cytomegalovirus (CMV) markedly alters the numbers and proportions of peripheral immune cells in infected-vs-uninfected people. Because the rate of CMV infection increases with age in most countries, it has been suggested that it drives or at least exacerbates "immunosenescence". This contention remains controversial and was the primary subject of the Third International Workshop on CMV & Immunosenescence which was held in Cordoba, Spain, 15-16th March, 2012. Discussions focused on several main themes including the effects of CMV on adaptive immunity and immunosenescence, characterization of CMV-specific T cells, impact of CMV infection and ageing on innate immunity, and finally, most important, the clinical implications of immunosenescence and CMV infection. Here we summarize the major findings of this workshop.

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)0.005
metaresearch head score (Gemma)0.005
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: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0040.006
Open science0.0010.004
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0050.001

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 head0.047
GPT teacher head0.359
Teacher spread0.312 · 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
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

Citations182
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueImmunity & AgeingSame topicCytomegalovirus and herpesvirus researchFrench-language works237,207