SF‐6Dv2 preference value set for health utility in food allergy
Bibliographic record
Abstract
Abstract Background The lack of a value set allowing the calculation of QALY is an important limitation when establishing the value of emerging therapies to treat food allergy. The aim of this study was to develop a Short‐Form Six‐Dimension version 2 (SF‐6Dv2) preference value set for the calculation of health utility from the Canadian food‐allergic population. Methods Two hundred ninety‐five parents of patients aged 0‐17 years old and 154 patients aged 12 years old and above with food allergy were recruited in clinic and online. Participants were asked to complete a self‐administered online questionnaire including generic health‐related quality of life questionnaires. Various health states described by the SF‐6Dv2 were valued with time‐trade‐off and discrete choice experiments. Data from elicitation techniques were combined using the hybrid regression model. Results A total of 241 parents and 125 patients performed 3904 time‐trade‐off and 5112 discrete choice experiments. Utility decrements were estimated for each level of each SF‐6Dv2 dimension. Utility values calculated based on the validated preference set were in average 0.15 lower (95%CI: 0.12‐0.18) and were poorly correlated (R2 = 0.46) with those derived from the EQ‐5D‐5L generic questionnaire in the same cohort. Conclusion A representative preference value set for patients with food allergy was determined using the SF‐6Dv2 generic questionnaire. This adapted preference set will contribute to improve the validity of future utility estimates in this population for the appraisal of upcoming potentially impactful but sometimes costly therapies.
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 | 0.013 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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) | 0.004 | 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".