Predicting postoperative epilepsy surgery satisfaction in adults using the 19‐item Epilepsy Surgery Satisfaction Questionnaire and machine learning
Bibliographic record
Abstract
ABSTRACT Objective The 19‐item Epilepsy Surgery Satisfaction Questionnaire (ESSQ‐19) is a validated and reliable post hoc means of assessing patient satisfaction with epilepsy surgery. Prediction models building on these data can be used to counsel patients. Methods The ESSQ‐19 was derived and validated on 229 patients recruited from Canada and Sweden. We isolated 201 (88%) patients with complete clinical data for this analysis. These patients were adults (≥18 years old) who underwent epilepsy surgery 1 year or more prior to answering the questionnaire. We extracted each patient’s ESSQ‐19 score (scale is 0–100; 100 represents complete satisfaction) and relevant clinical variables that were standardized prior to the analysis. We used machine learning (linear kernel support vector regression [SVR]) to predict satisfaction and assessed performance using the R2 calculated following threefold cross‐validation. Model parameters were ranked to infer the importance of each clinical variable to overall satisfaction with epilepsy surgery. Results Median age was 41 years (interquartile range [IQR] = 32–53), and 116 (57%) were female. Median ESSQ‐19 global score was 68 (IQR = 59–75), and median time from surgery was 5.4 years (IQR = 2.0–8.9). Linear kernel SVR performed well following threefold cross‐validation, with an R2 of .44 (95% confidence interval = .36–.52). Increasing satisfaction was associated with postoperative self‐perceived quality of life, seizure freedom, and reductions in antiseizure medications. Self‐perceived epilepsy disability, age, and increasing frequency of seizures that impair awareness were associated with reduced satisfaction. Significance Machine learning applied postoperatively to the ESSQ‐19 can be used to predict surgical satisfaction. This algorithm, once externally validated, can be used in clinical settings by fixing immutable clinical characteristics and adjusting hypothesized postoperative variables, to counsel patients at an individual level on how satisfied they will be with differing surgical outcomes.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".