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Modified Vaccination Technique for Prophylactic and Therapeutic Applications to Combat Endogenous Antigen–Induced Disorders

2009· review· en· W2023245523 on OpenAlexaff
A. Z. Barabas, Chad Cole, Richard Graeff, René Lafrenière, D. M. Weir

Bibliographic record

VenueScandinavian Journal of Immunology · 2009
Typereview
Languageen
FieldImmunology and Microbiology
TopicImmunotherapy and Immune Responses
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsVaccinationImmune systemContext (archaeology)ImmunologyEndogenyMedicineAntigenDiseaseBiologyInternal medicine

Abstract

fetched live from OpenAlex

Public health can be protected most effectively through vaccination programmes. However, while presently available vaccination techniques protects the individual by provoking immune responses against exogenous antigens (ags), such as those associated with certain bacteria and viruses, they cannot protect against or treat mishaps caused by endogenous ag. Recently, Barabas and colleagues have developed a new vaccination method, called modified vaccination technique (MVT), which allows the presentation of disease causing agents in such a way as to initiate and maintain desired immune response outcomes even in the context of mishaps associated with endogenous ag. For example, in an experimental autoimmune kidney disease, the MVT downregulated/terminated pathogenic immune responses that were causing morphological and functional changes of the kidney. The MVT promises, with appropriate case-specific modifications, both preventative and curative applications for ailments, such as endogenous ag initiated mishaps (i.e. autoimmune diseases and cancer) and diseases caused by chronic infection, that are presently only treatable with drugs. To achieve specific immune responses, purified components of the vaccine (ag and antibodies) must be produced and assembled into immune complexes having the potential of inducing predetermined corrective immune response outcomes.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.974
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.050
GPT teacher head0.324
Teacher spread0.274 · 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.

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

Citations6
Published2009
Admission routes1
Has abstractyes

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