Estimating minimal important differences for several scales assessing function and quality of life in patients with attention-deficit/hyperactivity disorder
Bibliographic record
Abstract
OBJECTIVE: Defining minimal important difference (MID) is critical to interpreting patient-reported outcomes data and treatment efficacy in clinical trials. This study estimates the MID for the Weiss Functional Impairment Rating Scale-Parent Report (WFIRS-P) and the Child Health and Illness Profile-Parent Report (CHIP-CE-PRF76) among parents of young people with attention-deficit/hyperactivity disorder (ADHD) in the UK. METHODS: Parents of children (6-12 years; n=100) and adolescents (13-17 years; n=117) with ADHD completed a socio-demographic form, the CHIP-CE-PRF76, the WFIRS-P, and the Pediatric Quality of Life scale at baseline and 4 weeks later. At follow-up, a subset of parents completed anchor questions measuring change in the child/adolescent from baseline. MIDs were estimated using anchor-based and distribution-based methods, and separately for children and adolescents. RESULTS: The MID estimates for overall change in the WFIRS-P total score ranged from 11.31 (standard error of measurement) to 13.47 (anchor) for the total sample. The range of MID estimates for the CHIP-CE-PRF76 varied by domain: 6.80-7.41 (satisfaction), 6.18-7.34 (comfort), 5.60-6.72 (resilience), 6.06-7.57 (risk avoidance), and 4.00-5.63 (achievement) for the total sample. Overall, MID estimates for WFIRS-P MID and CHIP-CE-PRF76 were slightly higher for adolescents than for children. CONCLUSION: This study estimated MIDs for these instruments using several methods. The observed convergence of the MID estimates increases confidence in their reliability and could assist clinicians and decision makers in deriving meaningful interpretations of observed changes in the WFIRS-P and CHIP-CE in clinical trials and practice.
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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 | 0.034 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".