Comprehensive Outcome Assessment and Quality of Life Following Epilepsy Surgery
Bibliographic record
Abstract
BACKGROUND: Seizure freedom without deficits is the primary goal for epilepsy surgery. However, patients with medically refractory epilepsy commonly suffer from many co-morbidities related to mood, cognition, and sleep as well as social problems and resultant stigma. While epilepsy surgery literature does describe quality of life (QOL) and neuropsychological outcomes, there is a paucity of information on various common non-seizure outcomes, especially pertaining to mood, sleep, cognition, and social aspects. The objective of this study was to evaluate the role of various non-seizure parameters on post-epilepsy surgery QOL. METHODS: Consecutive adult patients operated for refractory epilepsy at least 1 year prior to initiation of this study were included and classified as seizure-free (group 1) or non-seizure-free (group 2). QOL was assessed using the QOLIE-31 instrument; patients with a T score less than 40 were categorized as "poor QOL." Non-seizure parameters assessed were cognition, mood disturbances, social improvement, social stigma, and sleep disturbances. Categorization into "good" and "poor" outcome subgroups on each item was carried out by dichotomization of scores. RESULTS: Thirty-seven patients (16 F) [mean age 23.5 ± 5.6 years] were evaluated; 26 were seizure-free (group 1). In this group, impaired memory, lower language scores, depression, not having been employed, not receiving education prior to surgery, and experiencing social stigma were factors significantly associated with poor QOL. In group 2, all patients had poor QOL scores. CONCLUSION: Non-seizure factors related to common epilepsy co-morbidities and social issues are highly prevalent among seizure-free patients reporting poor QOL 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".