Development and validation of the Epilepsy Surgery Satisfaction Questionnaire (ESSQ‐19)
Bibliographic record
Abstract
OBJECTIVE: No validated tools exist to assess satisfaction with epilepsy surgery. We aimed to develop and validate a new measure of patient satisfaction with epilepsy surgery, the 19-item Epilepsy Surgery Satisfaction Questionnaire (ESSQ-19). METHODS: An initial 31-item measure was developed based on literature review, patient focus groups, thematic analysis, and Delphi panels. The questionnaire was administered twice, 4-6 weeks apart, to 229 adults (≥18 years old) who underwent epilepsy surgery ≥1 year earlier, at three centers in Canada and one in Sweden. Participants also completed seven validated questionnaires to assess construct validity. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) assessed the factorial structure of the questionnaire. Cronbach alpha and intraclass correlation coefficients (ICCs) assessed the internal consistency and test-retest reliability of the ESSQ-19. Spearman and polyserial correlations assessed construct validity. RESULTS: Median age of participants and time since surgery were 42 years (interquartile range [IQR] = 32-54) and 5 years (IQR = 2-8.75), respectively. EFA and CFA yielded 18 items that segregated into four domains (mean score [SD]), namely, seizure control (76.4 [25]), psychosocial functioning (67.3 [26]), surgical complications (84 [22]), and recovery from surgery (73 [24]), one global satisfaction item, and a summary global score (74 [21]). The domain and summary scores demonstrated good to excellent internal reliability (Cronbach ⍺ range = .84-.95) and test-retest reliability (ICC range = 0.71-0.85). Construct validity was supported by predicted correlations with other instruments. SIGNIFICANCE: The ESSQ-19 is a new, valid, and reliable measure of patient satisfaction with epilepsy surgery that can be used in clinical and research settings.
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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 | 0.021 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.003 | 0.001 |
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".