Bibliographic record
Abstract
BACKGROUND: Health related quality of life (HRQOL) is increasingly recognised as an important outcome in epilepsy. However, interpretation of HRQOL data is difficult because there is no agreement on what constitutes a clinically important change in the scores of the various instruments. OBJECTIVES: To determine the minimum clinically important change, and small, medium, and large changes, in broadly used epilepsy specific and generic HRQOL instruments. METHODS: Patients with difficult to control focal epilepsy (n = 136) completed the QOLIE-89, QOLIE-31, SF-36, and HUI-III questionnaires twice, six months apart. Patient centred estimates of minimum important change, and of small, medium, and large change, were assessed on self administered 15 point global rating scales. Using regression analysis, the change in each HRQOL instrument that corresponded to the various categories of change determined by patients was obtained. The results were validated in a subgroup of patients tested at baseline and at nine months. RESULTS: The minimum important change was 10.1 for QOLIE-89, 11.8 for QOLIE-31, 4.6 for SF-36 MCS, 3.0 for SF-36 physical composite score, and 0.15 for HUI-III. All instruments differentiated between no change and minimum important change with precision, and QOLIE-89 and QOLIE-31 also distinguished accurately between minimum important change and medium or large change. Baseline HRQOL scores and the type of treatment (surgical or medical) had no impact on any of the estimates, and the results were replicated in the validation sample. CONCLUSIONS: These estimates of minimum important change, and small, medium, and large changes, in four HRQOL instruments in patients with epilepsy are robust and can distinguish accurately among different levels of change. The estimates allow for categorisation of patients into various levels of change in HRQOL, and will be of use in assessing the effect of interventions in individual patients.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".