Measuring patient satisfaction following epilepsy surgery
Bibliographic record
Abstract
PURPOSE: To systematically review primary research examining patient satisfaction with epilepsy surgery in order to obtain evidence-based estimates of this surgical outcome; to assess methods used to measure epilepsy surgery satisfaction, overall epilepsy surgery satisfaction ratings, and predictors of epilepsy surgery satisfaction. METHODS: Systematic review of published studies in English up to June 2009, focusing on patient satisfaction with all types of epilepsy surgery in patients of all ages. We excluded studies that focused on satisfaction with epilepsy treatment in general, on quality of life without specific exploration of patient satisfaction with surgery, and on satisfaction with the process of health care delivery, rather than with surgery and its outcomes. KEY FINDINGS: Eight studies met inclusion criteria. Satisfaction was assessed using one or more global questions. Four epilepsy surgery satisfaction question content patterns emerged: (1) satisfied or dissatisfied, (2) perceived success or failure, (3) overall positive or negative impact, and (4) willingness to repeat surgery or regretting surgery. Overall 71% were satisfied; 64% considered it a success; it had a positive effect for 78%; and 87% would repeat surgery. Seizure freedom was the most common predictor of epilepsy surgery satisfaction, whereas postoperative neurologic deficit predicted dissatisfaction. SIGNIFICANCE: Patient satisfaction with interventions is an important aspect of patient-centered care, but it has received little attention in epilepsy surgery. Future research is required to develop and validate epilepsy surgery satisfaction tools. We provide preliminary guiding principles for measuring satisfaction after epilepsy surgery.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".