Spanish translation, adaptation, and validation of the Epilepsy Surgery Satisfaction Questionnaire‐19
Bibliographic record
Abstract
OBJECTIVE: The effectiveness of surgery in drug-resistant epilepsies is often focused exclusively on seizure control. The Epilepsy Surgery Satisfaction Questionnaire-19 (ESSQ_19), developed in 2020, is a reliable tool for assessing the level of satisfaction of patients undergoing surgery. We aimed to perform a Spanish translation, adaptation, and validation of the ESSQ_19 questionnaire. METHODS: This was a prospective multicenter study (five Spanish-speaking countries) including an international cohort of adult patients who underwent epilepsy surgery at least 1 year prior to participation. We followed a translation and back-translation methodology to obtain the Spanish version of the questionnaire (ESP-ESSQ_19). For validation, internal consistency (Cronbach alpha) and test-retest reliability at 6 months were assessed, and psychometric properties were measured by correlating them with parallel aspects using validated questionnaires. RESULTS: One hundred fifty patients were included (59.3% women; mean age = 39.3 ± 13 years, interquartile range [IQR] = 28-49 years), of whom 94 completed the baseline and follow-up questionnaires, with a median time since surgery of 3 (IQR = 1-7) years. Temporal lobe epilepsy was the most common (n = 112, 77.8%), as was structural etiology (n = 139, 95.2%). At inclusion, 101 patients (68.7%) had been seizure-free for ≥1 year. The ESP-ESSQ_19 questionnaire showed an internal consistency index of .88-.95 for all domains and a test-retest reliability of .91 (95% confidence interval = .87-.94). Significant correlation coefficients were observed between the ESP-ESSQ_19 questionnaire and all validated questionnaires used. SIGNIFICANCE: The ESP-ESSQ_19 questionnaire is a Spanish-language instrument useful for assessing patient satisfaction after epilepsy surgery, with adequate psychometric properties that allow its use in clinical practice and in 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 | 0.005 | 0.007 |
| 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 | 0.000 |
| 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".