Patient satisfaction with epilepsy surgery: what is important to patients
Bibliographic record
Abstract
Patient satisfaction with therapeutic interventions is an important outcome of care. Although generic measures of patient satisfaction exist, there is no validated scale for measuring patient satisfaction with epilepsy surgery. We aimed to systematically obtain patient-identified factors related to satisfaction with epilepsy surgery as a means of informing clinicians about the ways that patients evaluate outcomes of their treatment and as a conceptual basis for the future development of epilepsy surgery patient satisfaction scales. Focus group discussions with epilepsy surgery patients (n=9) were conducted to identify themes relevant to patient satisfaction with epilepsy surgery and to draft initial items of importance. Consensus methodology (Delphi technique) was used to obtain expert opinion (n=13) to refine the items. Member-checking with focus group participants was performed to ensure the identified items were relevant, clear, and inclusive. A list of 31 items embodied 12 themes related to patient-reported satisfaction with epilepsy surgery. These included adverse effects, medical care or rehabilitation, seizure control, post-operative recovery, anti-seizure medication, independence, seizure worry, ability to drive, social relationships, self-confidence, improved cognitive function, and improved physical health. This study used a systematic approach to identify factors that are important to patients when assessing satisfaction with epilepsy surgery. This knowledge can assist clinicians caring for these patients and is also a critical step towards the validation of a formal scale to assess satisfaction with 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 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".