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Record W2318403340 · doi:10.2500/aap.2014.35.3758

The history and progression of treatments for allergic rhinitis

2014· review· en· W2318403340 on OpenAlexaboutno aff
Nancy K. Ostrom

Bibliographic record

VenueAllergy and Asthma Proceedings · 2014
Typereview
Languageen
FieldMedicine
TopicAllergic Rhinitis and Sensitization
Canadian institutionsnot available
Fundersnot available
KeywordsAntihistamineMedicineContext (archaeology)Intensive care medicineAsthmaPharmacologyInternal medicine

Abstract

fetched live from OpenAlex

This article intends to place new treatments in the context of allergic rhinitis (AR) treatment history. The medical literature was searched for significant advances and changes in AR treatment. Historical data on AR treatment options and management were selected. Reviews of AR management published throughout the 20th century were included to provide context for treatment advances. Modern AR treatment began in the early 20th century with immunotherapy and was soon followed by the emergence of antihistamine therapy in the 1930s. Numerous treatments for AR have been used over the ensuing decades, including decongestants, mast cell stabilizers, and leukotriene receptor antagonists. Topical corticosteroid options were developed the 1950s, and, added to baseline antihistamine therapy, became the foundation of AR treatment. Treatment options were significantly impacted after the 1987 Montreal Protocol, which phased out the use of chlorofluorocarbon propellant aerosols because of environmental concerns. From the mid-1990s until recently, this left only aqueous solution options for intranasal corticosteroids (INSs). The approval of the first hydrofluoroalkane propellant aerosol INSs for AR in 2012 restored a "dry" aerosol treatment option. The first combination intranasal antihistamine/INSs was also approved in 2012, providing a novel treatment option for AR. Treatment of AR has progressed with new therapeutic options now available. This should continue to move forward with agents to alter the allergic mechanism itself and impact the disease burden that has a significant impact on patient 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score0.771

Codex and Gemma teacher scores by category

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 integrity0.0000.000
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 head0.029
GPT teacher head0.300
Teacher spread0.271 · 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 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

Citations15
Published2014
Admission routes1
Has abstractyes

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