Psychometric parameters of food allergy quality of life during an allergen immunotherapy trial
Bibliographic record
Abstract
BACKGROUND: The Food Allergy Quality of Life Questionnaire-Parent Form (FAQLQ-PF) is a commonly used patient-reported outcome measure in food allergy (FA) research. It was developed before FA treatment clinical trials were commonplace and is used as a secondary outcome measure in pivotal FA treatment trials. We examined the psychometric properties of the FAQLQ-PF and its relevance to children with peanut allergy engaged in an epicutaneous immunotherapy (EPIT) clinical trial. METHODS: Analysis was performed on 26 universally answered items of the FAQLQ-PF, from assessments undertaken during the phase 3 PEPITES study (baseline, Month 12), which examined the safety and efficacy of EPIT for children with peanut allergy aged 4-11 years. Item response theory (IRT) was used to assess psychometric parameters of the FAQLQ-PF (i.e., discrimination, difficulty, and information). Confirmatory factor analysis was also employed; reliability was assessed using McDonald's omega (ω) and Cronbach's alpha (α). RESULTS: A total of 23 of 26 items presented very high discrimination levels (>1.7), and all 26 fell within the recommended difficulty threshold (between -1.5 and 1.5). The items contributed a reasonable information level for their respective factors/subdomains. The measure also presented a marginally acceptable model fit for the 3-factor structure (e.g., comparative fit index = 0.88, Tucker-Lewis index = 0.87) and good reliability levels across time points (ω and α > 0.90). CONCLUSIONS: Herein, we present a novel reanalysis of the FAQLQ-PF items using IRT. The longitudinal performance of individual items and subscales was corroborated, and items with the highest discrimination were identified, showing that the tool is suitable for longitudinal measurements in FA treatment trials.
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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 | 0.041 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".