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Record W1485882517 · doi:10.1002/9781118663721.ch4

Answers to Common Misconceptions Regarding the Toxicity of Aluminum Adjuvants in Vaccines

2015· other· en· W1485882517 on OpenAlexaff
Lucija Tomljenovic, Christopher A. Shaw

Bibliographic record

Venuenot available
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicAluminum toxicity and tolerance in plants and animals
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAdjuvantInflammasomeVaccine adjuvantImmunologyImmune systemInnate immune systemToxicityAction (physics)MedicineNeuroscienceBiologyInflammation

Abstract

fetched live from OpenAlex

This chapter presents answers to the most common misconceptions regarding the safety of aluminium (Al) compounds in vaccines by providing an overview of what is currently known about Al adjuvants, in particular, and their modes of action and mechanisms of potential toxicity. It gives a brief overview of the crucial role of Al in a variety of neurological disorders and then elaborates on the unresolved controversy about Al adjuvant safety. Al adjuvants exert their immunostimulatory effect through many different actions, which impinge on both the innate and adaptive immune systems. Some of the actions are activation of the NLRP3 inflammasome pathway, and protection of antigens, resulting in prolonged delivery. The risks associated with vaccine-derived Al are threefold: it can persist in the body, it can trigger pathological immunological responses, and it can make its way into the central nervous system (CNS).

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)0.015
metaresearch head score (Gemma)0.023
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: Other · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.007
Scholarly communication0.0030.006
Open science0.0010.003
Research integrity0.0040.009
Insufficient payload (model declined to judge)0.0050.002

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 head0.031
GPT teacher head0.259
Teacher spread0.227 · 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
GenreOther

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

Citations5
Published2015
Admission routes1
Has abstractyes

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