Development and validation of an electronic version of the Rhinoconjunctivitis Quality of Life Questionnaire
Bibliographic record
Abstract
BACKGROUND: As clinicians and pharmaceutical companies move from paper versions of health status questionnaires to electronic versions, it cannot be assumed that adaptations to other media will produce valid data. AIMS: The aims of this study were to (1) adapt the Rhinoconjunctivitis Quality of Life Questionnaire [RQLQ(S); standardized version], for the Palm Treo 650, (2) test the device for ease and accuracy of understanding and (3) examine the validity of the electronic version by comparing it with the original paper version of the RQLQ(S). METHODS: Seventy adults with current rhinoconjunctivitis symptoms completed the electronic and paper versions of the RQLQ(S). They were randomized to complete either the paper or the electronic version first. After a 2-h break, they completed the other version. RESULTS: Concordance between paper and electronic versions for the overall RQLQ(S) score was acceptable with an intraclass correlation coefficient of 0.95 and there was no evidence of bias (P = 0.13). Concordance for the seven individual domains ranged from 0.86 to 0.94. A small but significant bias was observed in the activity and sleep domains (P = 0.02). Completion times were quicker with paper (4.1 vs 4.9 min, P < 0.0001). About 51% of patients preferred electronic, 17% preferred paper and 31% had no preference. CONCLUSIONS: This electronic version of the RQLQ(S) was easy for patients to use and the concordance between paper and this version on the Palm Treo 650 provides evidence of the validity of this electronic version.
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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 | 0.027 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".