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Record W2733464133 · doi:10.1093/geroni/igx004.4677

MECHANISMS OF PROTECTION AGAINST HERPES ZOSTER AND POST-HERPETIC NEURALGIA THROUGH VACCINATION

2017· article· en· W2733464133 on OpenAlexaff
Janet E. McElhaney

Bibliographic record

VenueInnovation in Aging · 2017
Typearticle
Languageen
FieldMedicine
TopicHuman Health and Disease
Canadian institutionsHealth Sciences North
Fundersnot available
KeywordsMedicineChickenpoxVaricella zoster virusRashShinglesImmunologyVirusVirologyVaccinationDorsal root ganglionNeuralgiaDermatologyNeuropathic painDorsumAnesthesia

Abstract

fetched live from OpenAlex

Herpes zoster (or Shingles) is a painful blistering rash resulting from the reactivation of latent Varicella-zoster virus (VZV), the agent that causes of chickenpox. With the resolution of chickenpox, VZV-specific cytotoxic T lymphocytes (CTL) access the dorsal root ganglion where VZV lives, to keep viral replication in check. VZV-specific CD4+ and CD8+ T cells are believed to play a central role in latency and reactivation of the virus within the dorsal root ganglion. Reactivation of latent VZV is associated with marked inflammation of the sensory ganglion leading to nerve cell damage and pain that often precedes the onset of the dermatomal rash and persists after the rash resolves. The risk of developing zoster the disabling complication of post-herpetic neuralgia (PHN) dramatically increases with age. These observations highlight the importance of designing vaccines to stimulate cell-mediated immunity to prevent reactivation of VZV and protect against zoster and PHN, rather than the usual focus on antibody responses to prevent infection. Vaccines designed to prevent zoster reactivation and PHN present a significant opportunity for vaccine preventable disability in older adults.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.042
GPT teacher head0.338
Teacher spread0.297 · 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 designTheoretical or conceptual
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

Citations0
Published2017
Admission routes1
Has abstractyes

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