Establishing the minimum clinically important difference of the Quality of Life in Childhood Epilepsy Questionnaire
Bibliographic record
Abstract
OBJECTIVE: To estimate the minimum clinically important difference (MCID) for the parent-reported 55-item Quality of Life in Childhood Epilepsy Questionnaire (QOLCE-55) and its shortened 16-item version, QOLCE-16. METHODS: Data came from 74 children with epilepsy (CWE) (ages 4-10, mean age = 8 [SD = 1.8]) enrolled in the Making Mindfulness Matter in Epilepsy (M3-E) trial, a pilot, parallel randomized-controlled trial of a mindfulness-based intervention. Both anchor-based and distribution-based methods were used to estimate MCID values for the QOLCE-55 and QOLCE-16. For the anchor-based approach, the Patient Centered Global Ratings of Change (PCGRC) scale and linear regression analysis were used to estimate the MCID. For the distribution-based approach, .5 SD of the health-related quality of life (HRQOL) change score distribution was used to estimate the MCID. RESULTS: For the QOLCE-55, the MCID obtained using an anchor-based approach was 10 points and using a distribution-based method was 6 points. For the QOLCE-16, the MCID obtained using an anchor-based method was 13 points and using a distribution-based method was 7 points. SIGNIFICANCE: This is the first study to estimate MCID values for the QOLCE-55 and the QOLCE-16. It has been well documented that CWE are at risk of experiencing psychological, behavioral, and cognitive impairments, which can negatively impact their HRQOL. Reporting MCID values for the QOLCE-55 and QOLCE-16 is important in determining whether changes in HRQOL observed are meaningful to CWE themselves, as a key factor in shaping the nature of epilepsy care delivered.
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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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".