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Record W2065570358 · doi:10.1186/1710-1492-6-26

The anti-vaccination movement and resistance to allergen-immunotherapy: a guide for clinical allergists

2010· article· en· W2065570358 on OpenAlexafffundvenue
Jason Behrmann

Bibliographic record

VenueAllergy Asthma and Clinical Immunology · 2010
Typearticle
Languageen
FieldMedicine
TopicAllergic Rhinitis and Sensitization
Canadian institutionsUniversité de Montréal
FundersSocial Sciences and Humanities Research Council of CanadaUniversité de Montréal
KeywordsVaccinationMedicineImmunotherapyAllergen immunotherapyAllergyImmunologyRegimenPublic healthFamily medicineAllergenIntensive care medicineInternal medicineImmune system

Abstract

fetched live from OpenAlex

Despite over a century of clinical use and a well-documented record of efficacy and safety, a growing minority in society questions the validity of vaccination and fear that this common public health intervention is the root-cause of severe health problems. This article questions whether growing public anti-vaccine sentiments might have the potential to spill-over into other therapies distinct from vaccination, namely allergen-immunotherapy. Allergen-immunotherapy shares certain medical vernacular with vaccination (e.g., allergy shots, allergy vaccines), and thus may become "guilty by association" due to these similarities. Indeed, this article demonstrates that anti-vaccine websites have begun unduly discrediting this allergy treatment regimen. Following an explanation of the anti-vaccine movement, the article aims to provide guidance on how clinicians can respond to patient fears towards allergen-immunotherapy in the clinical setting. This guide focuses on the provision of reliable information to patients in order to dispel misconceived associations between vaccination and allergen-immunotherapy, and the discussion of the risks and benefits of both therapies in order to assist patients in making autonomous decisions about their choice of allergy treatment.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.720
Threshold uncertainty score0.710

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.023
GPT teacher head0.355
Teacher spread0.332 · 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

Citations11
Published2010
Admission routes3
Has abstractyes

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