Changes in Quality of Life in Epilepsy: How Large Must They Be to Be Real?
Bibliographic record
Abstract
PURPOSE: The study goal was to assess the magnitude of change in generic and epilepsy-specific health-related quality-of-life (HRQOL) instruments needed to exclude chance or error at various levels of certainty in patients with medically refractory epilepsy. METHODS: Forty patients with temporal lobe epilepsy and clearly defined criteria of clinical stability received HRQOL measurements twice, 3 months apart, using the Quality of Life in Epilepsy Inventory-89 and -31 (QOLIE-89 and QOLIE-31), Liverpool Impact of Epilepsy, adverse drug events, seizure severity scales, and the Generic Health Utilities Index (HUI-III). Standard error of measurement and test-retest reliability were obtained for all scales and for QOLIE-89 subscales. Using the Reliable Change Index described by Jacobson and Truax, we assessed the magnitude of change required by HRQOL instruments to be 90 and 95% certain that real change has occurred, as opposed to change due to chance or measurement error. RESULTS: Clinical features, point estimates and distribution of HRQOL measures, and test-retest reliability (all > 0.70) were similar to those previously reported. Score changes of +/-13 points in QOLIE-89, +/-15 in QOLIE-31, +/-6.3 in Liverpool seizure severity-ictal, +/-11 in Liverpool adverse drug events, +/-0.25 in HUI-III, and +/-9.5 in impact of epilepsy exclude chance or measurement error with 90% certainty. These correspond, respectively, to 13, 15, 17, 18, 25, and 32% of the potential range of change of each instrument. CONCLUSIONS: Threshold values for real change varied considerably among HRQOL tools but were relatively small for QOLIE-89, QOLIE-31, Liverpool Seizure Severity, and adverse drug events. In some instruments, even relatively large changes cannot rule out chance or measurement error. The relation between the Reliable Change Index and other measures of change and its distinction from measures of minimum clinically important change are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".