Review of Symptoms Assessment During Nasal Allergen Provocation in Patients with Allergic Rhinitis
Bibliographic record
Abstract
Background: Allergic rhinitis is the most prevalent allergic disease. Nasal provocation tests (NPTs) may be useful for its clinical diagnostic and therapy monitoring although they are mostly used in clinical research. However, the lack of standardisation in the symptoms assessed and the variety of instruments used make effective comparison between studies difficult. Objective: To review the published literature searching for instruments assessing nasal symptoms during NPTs for allergic rhinitis. Methods: Pubmed and Embase electronic databases were reviewed, looking for all methods including an instrument assessing symptoms during or following NPTs. Studies on animal models, pediatric subjects, and patients without allergic rhinitis were excluded. Studies were also excluded if they did not assess nasal symptoms during or following the NPT. Only NPT studies performed with allergen extracts or histamine were included. Results: A total of 520 studies were retrieved, from which 81 different instruments from 81 studies were included in the present analysis. There was no instrument reporting a validation process for the assessment of symptoms during NPTs. From the remaining instruments, the most common symptoms assessed were rhinorrhea (67), sneezing (70), congestion (67), and nasal pruritus (50). The most frequently used type of scales among those instruments was the four-point Likert scale (39), although different methods were used. Conclusions: This review illustrates the large variety of symptoms and methods used to assess the aforementioned NPTs. The lack of validation studies suggests the need to develop and validate a standardized instrument assessing symptoms following NPTs.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".