Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".