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Record W1990408126 · doi:10.2217/ahe.12.78

The Immune System in the Elderly: A Fair Fight Against Diseases?

2013· article· en· W1990408126 on OpenAlexaff
Anis Larbi, Paulina Dominika Rymkiewicz, Anusha Vasudev, Ivy Low, Nurhidaya Binte Shadan, Seri Mustafah, Shamini Ayyadhury, Tamàs Fülöp

Bibliographic record

VenueAging Health · 2013
Typearticle
Languageen
FieldMedicine
TopicCytomegalovirus and herpesvirus research
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsImmune systemMedicineImmunologyAcquired immune systemInnate immune systemNeuroscienceBiology

Abstract

fetched live from OpenAlex

The immune system is a very dynamic network, consisting of various innate and adaptive cells acting via direct cell–cell contact, as well as indirect messaging mediated by soluble factors. Changes in the number or quality of receptors involved in these events alter the capacity of the immune system to respond properly. There is an increasing awareness that many chronic infections and diseases, such as cytomegalovirus, impact on the immune system. Concurrently, there are changes in immune profiles of healthy, as well as ill, elderly individuals. All these changes translate into obvious signs of immunological aging that are more profound in diseases. This review will discuss the manifestation of different diseases in the elderly and how the immune system behaves in such situations.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.845
Threshold uncertainty score0.819

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.022
GPT teacher head0.321
Teacher spread0.299 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations29
Published2013
Admission routes1
Has abstractyes

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