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Record W1994983985 · doi:10.1186/1710-1492-2-3-117

Local Nasal Specific Immunotherapy for Allergic Rhinitis

2006· article· en· W1994983985 on OpenAlexvenueno aff
Giovanni Passalacqua, Giorgio Walter Canonica

Bibliographic record

VenueAllergy Asthma and Clinical Immunology · 2006
Typearticle
Languageen
FieldMedicine
TopicAllergic Rhinitis and Sensitization
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineNasal administrationImmunotherapySublingual immunotherapyClinical trialNoseImmunologyClinical efficacyDesensitization (medicine)DermatologyAllergySurgeryInternal medicineImmune systemReceptor

Abstract

fetched live from OpenAlex

The possibility of producing local hyposensitization by administering allergens via mucosal routes was envisaged at the beginning of 1900, and local nasal immunotherapy has been extensively studied since the 1970s. Presently, there are 21 randomized controlled trials being conducted with the most common allergens, consistently showing the clinical efficacy of local nasal immunotherapy for rhinitis. Other advantages are that it has an optimal safety profile and can be self-administered at home by the patient. Moreover, there are several data from animal models and from humans that confirm the immunomodulatory effect of intranasally administered antigens. On the other hand, local nasal immunotherapy seems to be effective only on rhinitis symptoms and requires a particular technique of administration. For these reasons, its clinical use is progressively declining in favour of the sublingual route although nasal immunotherapy is validated in official documents and remains a viable alternative to injection.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0050.002

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 head0.020
GPT teacher head0.293
Teacher spread0.273 · 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 designObservational
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

Citations24
Published2006
Admission routes1
Has abstractyes

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