Next-Generation Allergic Rhinitis Care in Singapore: 2019 ARIA Care Pathways
Bibliographic record
Abstract
Allergic rhinitis (AR) is prevalent in Singapore, with a significant disease burden. Afflicting up to 13% of the population, AR impairs quality of life, leads to reduced work productivity and is an independent risk factor for asthma. In the last 2 decades, local studies have identified patient and physician behaviours leading to suboptimal control of the disease. Yet, there is an overall lack of attention to address this important health issue. Allergic Rhinitis and its Impact on Asthma (ARIA) is a European organisation aimed at implementing evidence-based management for AR worldwide. Recent focus in Europe has been directed towards empowering patients for self-management, exploring the complementary role of mobile health, and establishing healthcare system-based integrated care pathways. Consolidation of these ongoing efforts has led to the release of the 2019 ARIA care pathways. This review summarises the ARIA update with particular emphasis on the current status of adult AR in Singapore. In addition, we identify unmet needs and future opportunities for research and clinical care of AR in the local context.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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.
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".