Validity and reliability of global ratings of satisfaction with epilepsy surgery
Bibliographic record
Abstract
OBJECTIVE: We aimed to assess the reliability and validity of single-item global ratings (GR) of satisfaction with epilepsy surgery. METHODS: We recruited 240 patients from four centers in Canada and Sweden who underwent epilepsy surgery ≥1 year earlier. Participants completed a validated questionnaire on satisfaction with epilepsy surgery (the ESSQ-19), plus a single-item GR of satisfaction with epilepsy surgery twice, 4-6 weeks apart. They also completed validated questionnaires on quality of life, depression, health state utilities, epilepsy severity and disability, medical treatment satisfaction and social desirability. Test-retest reliability of the GR was assessed with the intra-class correlation coefficient (ICC). Construct and criterion validity were examined with polyserial correlations between the GR measure of satisfaction and validated questionnaires and with the ESSQ-19 summary score. Non-parametric rank tests evaluated levels of satisfaction, and ROC analysis assessed the ability of GRs to distinguish among clinically different patient groups. RESULTS: Median age and time since surgery were 42 years (IQR 32-54) and 5 years (IQR 2-8), respectively. The GR demonstrated good to excellent test-retest reliability (ICC = 0.76; 95% CI 0.67-0.84) and criterion validity (0.85; 95% CI 0.81-0.89), and moderate correlations in the expected direction with instruments assessing quality of life (0.59; 95% CI 0.51-0.63), health utilities (0.55; 95% CI 0.45-0.65), disability (-0.51; 95% CI -0.41, -0.61), depression (-0.48; 95% CI -0.38, -0.58), and epilepsy severity (-0.48; 95% CI -0.38, -0.58). As expected, correlations were lower for social desirability (0.40; 95% CI 0.28-0.52) and medical treatment satisfaction (0.33; 95% CI 0.21-0.45). The GR distinguished participants who were seizure-free (AUC 0.75; 95% CI 0.67-0.82), depressed (AUC 0.75; 95% CI 0.67-0.83), and self-rated as having more severe epilepsy (AUC 0.78; 95% CI 0.71-0.85) and being more disabled (AUC 0.82; 95% CI 0.74-0.90). SIGNIFICANCE: The GR of epilepsy surgery satisfaction showed good measurement properties, distinguished among clinically different patient groups, and appears well-suited for use in clinical practice and research.
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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 | 0.006 | 0.018 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".