Mapping the Food Allergy Quality of Life Questionnaire Parent Form onto the Short‐Form Six‐Dimensions version 2
Bibliographic record
Abstract
BACKGROUND: The Food Allergy Quality of Life Questionnaire Parent Form (FAQLQ-PF) is the most widely used quality of life questionnaire in food allergy. The objective of this study was to develop a mapping algorithm to convert FAQLQ-PF scores into health state utilities. METHODS: The Short-Form Six-Dimensions version 2 (SF-6Dv2) and FAQLQ-PF questionnaires were collected from an academic center oral immunotherapy referral cohort. Utility estimates were derived from the SF-6Dv2 using the food allergy preference set. Candidate mapping algorithm models were developed using seven regression methods starting from either the total average score, the average scores of each of the three domains or the individual item scores of FAQLQ-PF. The process was repeated twice, including only section A, common to all age groups, or including all age-applicable sections of the FAQLQ-PF. The mean absolute error (MAE) and root mean squared error (RMSE) were used to select the best fitting model. An independent cohort from a previous national online survey was used for external validation. RESULTS: In the index cohort, 1000 of 1257 respondents had completed both questionnaires. The lowest MAE (0.0791) and RMSE (0.1020) were recorded when entering individual item scores in a categorical regression model. The model including only FAQLQ-PF section A was found to be most consistent when tested in the external validation cohort (n = 248) (MAE of 0.0898). CONCLUSION: The FAQLQ-PF was mapped onto SF-6Dv2 utilities with good predictive accuracy in two independent cohorts. This will enable calculation of health utility for cost-effectiveness analyses in food allergy.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".