The Rhinitis Control Scoring System: Development and Validation
Bibliographic record
Abstract
BACKGROUND: Allergic rhinitis is a common health problem that requires regular monitoring of symptoms to provide adequate treatment. There is a need to develop rhinitis control assessment tools that are meaningful and easy to interpret by both the patient and the practitioner. OBJECTIVE: To develop a simple, easy-to-interpret instrument, the Rhinitis Control Scoring System (RCSS), for the assessment of rhinitis control, as a companion tool to the Asthma Control Scoring System previously validated. METHODS: After a literature review and based on the Allergic Rhinitis and its Impact on Asthma guidelines, allergic rhinitis control parameters were identified. The draft items were subjected to cognitive debriefing regarding instructions, wordings, and response options. The second version of the draft was then pretested and modified according to the results. The final draft of the RCSS, based on the intensity and frequency of symptoms, was then pilot tested with 50 subjects who had allergic rhinitis for validation of some psychometric properties. Each subject completed the RCSS in addition to other rhinitis instruments. They also had nasal peak inspiratory flow measurements. RESULTS: The RCSS showed good internal consistency (Cronbach α = 0.84). There was strong criterion validity between the RCSS scores and the other instruments. The discriminant validity demonstrated as mean RCSS scores differed significantly across groups of patients with different Total Nasal Symptom Score severity (F = 58.8, p < 0.0001). CONCLUSIONS: This pilot study showed that the RCSS is a simple tool to assess and quantify rhinitis control by using a percentage score. This questionnaire allows the quantification of rhinitis control and, therefore, may help guide therapeutic interventions. Combined with the Asthma Control Scoring System, it can provide a global assessment of rhinitis and asthma control. Clinical Trial number NCT00967967.
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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 | 0.037 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".