Why generic and disease‐specific quality‐of‐life instruments should be used together for the evaluation of patients with persistent allergic rhinitis
Bibliographic record
Abstract
BACKGROUND: The importance of assessing health-related quality of life (HRQL) of patients with allergic rhinitis (AR) has been well established, but the specific roles of rhinitis-specific or general health instruments have not been delineated. OBJECTIVE: We analysed the psychometric properties of a disease-specific instrument, the Rhinoconjunctivitis Quality-of-Life Questionnaire (RQLQ) and the general health instrument, the Medical Outcome Short-Form 36 (SF-36) as they are employed in combination in patients with persistent AR in clinical practice. METHOD: We analysed the data collected from a prospective study of 43 newly diagnosed patients with persistent AR and 44 controls. We interviewed the patients four times, at baseline, weeks 4, 8 and 10. RESULTS: The RQLQ and SF-36 have good discriminative property, internal consistency, and test-retest reliability. The RQLQ is superior to the SF-36 as an evaluative instrument because more of its domains respond to change, the magnitude of change was greater, and the response was faster. The SF-36 is more susceptible to floor and ceiling effects. Both instruments are unsuitable for mildly symptomatic patients based on Rasch model analysis. Each questionnaire assesses a distinct and significant portion of the total HRQL of persistent AR. CONCLUSION: The SF-36 and RQLQ are good for discriminating rhinitis patients from controls, but the former is poor for detecting changes in QOL. Both are inappropriate for mildly symptomatic patients. Each instrument measures non-overlapping halves of the measurable HRQL. For an assessment of the HRQL in persistent AR that is complete and responsive both instruments should be employed together.
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 | 0.027 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".