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Record W2132466754 · doi:10.1186/1742-4933-7-13

Immunosenescence and Cytomegalovirus: where do we stand after a decade?

2010· editorial· en· W2132466754 on OpenAlexaff
Graham Pawelec, Arne N. Akbar, Peter C. L. Beverley, Calogero Caruso, Evelyna Derhovanessian, Tamàs Fülöp, Paul Griffiths, Beatrix Grubeck‐Loebenstein, Klaus Hamprecht, Gerhard Jahn, Florian Kern, Sven D. Koch, Anis Larbi, Andrea B. Maier, Derek C. Macallan, Paul Moss, Sandrine Samson, Jan Strindhall, Emanuelle Trannoy, Mark R. Wills

Bibliographic record

VenueImmunity & Ageing · 2010
Typeeditorial
Languageen
FieldMedicine
TopicCytomegalovirus and herpesvirus research
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsImmunosenescenceCytomegalovirusImmunologyImmune systemMedicineHuman cytomegalovirusVirusVirologyHerpesviridaeViral disease

Abstract

fetched live from OpenAlex

AbstractSince Looney at al. published their seminal paper a decade ago it has become clear that many of the differences in T cell immunological parameters observed between young and old people are related to the age-associated increasing prevalence of infection with the persistent beta-herpesvirus HHV-5 (Cytomegalovirus). Ten years later, studies suggest that hallmark age-associated changes in peripheral blood T cell subset distribution may not occur at all in people who are not infected with this virus. Whether the observed changes are actually caused by CMV is an open question, but very similar, rapid changes observed in uninfected patients receiving CMV-infected kidney grafts are consistent with a causative role. This meeting intensively discussed these and other questions related to the impact of CMV on human immune status and its relevance for immune function in later life.

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)0.006
metaresearch head score (Gemma)0.013
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: Editorial · Consensus signal: Editorial
Teacher disagreement score0.013
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.013
Meta-epidemiology (narrow)0.0040.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0030.002
Science and technology studies0.0020.003
Scholarly communication0.0060.007
Open science0.0030.002
Research integrity0.0130.019
Insufficient payload (model declined to judge)0.0070.006

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 head0.014
GPT teacher head0.310
Teacher spread0.296 · 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
GenreEditorial

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

Citations88
Published2010
Admission routes1
Has abstractyes

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