Outcome of diagnostic intervention predicts health-related quality of life scores among children with food allergy
Bibliographic record
Abstract
Access to diagnostic care, regardless of diagnostic outcome, may attenuate the negative impact of food allergy on health-related quality of life (HRQL). We sought to determine if improved HRQL can be demonstrated among children, 0-12 years, who receive diagnostic care for food allergy in an allergy clinic setting. Parents attending clinic with their child completed the Food Allergy Quality of Life Questionnaire Parent Form before and after their visit. Parents with children on the clinic waitlist served as controls. HRQL scores were analyzed according to visit outcome: fewer or same number of food allergies. A sub-analysis of scores among children who underwent an oral food challenge (OFC) was conducted. The General Linear Model for Repeated Measures was used to compare changes in score over time between outcomes, and to test for interaction between score changes and outcomes. Mean pre-/post-visit scores were 1.93/1.68 for fewer (n = 64), 2.37/2.37 for same (n = 36), and 1.70/1.79 for controls (n = 59). Interaction between score change and visit outcome was significant (F 3.355, p = 0.037). Pre-/post-visit scores for OFC outcomes only were 2.24/2.03 for fewer (n = 35) and 2.03/2.53 for same (n = 10) number of food allergies. Interaction between score change and OFC outcome was significant (F 5.518, p = 0.023). Improvement in HRQL associated with food allergy diagnostic care appears to be dependent on visit outcome. Diagnosis of fewer food allergies predicted improvement in HRQL scores among children; this improvement may be most pronounced among those who receive oral food challenges.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".