Development and validation of the Mini Rhinoconjunctivitis Quality of Life Questionnaire
Bibliographic record
Abstract
BACKGROUND: The 28-item Rhinoconjunctivitis Quality of Life Questionnaire (RQLQ) has strong measurement properties but for large clinical trials, surveys and practice monitoring, where high efficiency is important, a shorter questionnaire is needed. OBJECTIVE: To develop and validate an abbreviated version of the RQLQ. METHODS: Using five RQLQ databases, items with high item-item correlations were combined and then the highest scoring items were selected for the MiniRQLQ (14 questions). There are five domains: activity limitations (standardized), practical problems and nose symptoms, eye symptoms and other symptoms. The MiniRQLQ, which is self-administered, was tested in a 5-week observational study in 100 adults with symptomatic rhinoconjunctivitis. Patients completed the MiniRQLQ, the RQLQ, and other measures of health status at baseline, 1 and 5 weeks. RESULTS: In patients whose rhinoconjunctivitis was stable between clinic visits, reliability (reproducibility and ability to discriminate between patients of different impairment) was very acceptable for the MiniRQLQ (ICC = 0.93) but not quite as good as for the RQLQ (ICC = 0.97). Responsiveness to change in clinical status was better with the MiniRQLQ than the RQLQ (P = 0. 044). Construct validity (correlation with other indices of health status) was strong for both the MiniRQLQ and the RQLQ. Concordance between the two instruments was high (ICC = 0.87). CONCLUSIONS: The MiniRQLQ has strong measurement properties and measures the same construct as the original RQLQ. The choice of questionnaire should depend on the task at hand.
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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 | 0.020 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".