34 Executive Function as a Protective Factor for Post-Surgical Quality of Life in Unilateral Epilepsy Surgery
Bibliographic record
Abstract
Objective: Many epilepsy syndromes are medically refractory, leading patients to be referred for surgical work-up to control their seizures and improve their quality of life (QOL). Although surgical treatments may reduce or stop seizures, many patients continue to present with declines in mood and/or cognition post-operatively. In addition, pre-operative QOL of patients with medically refractory epilepsy is impacted by executive function (EF). The present study aims to investigate the relationship between post-operative mood/QOL and pre-operative EF in adults with epilepsy. It was hypothesized that mood would remain stable or decline post-operatively; pre-operative EF would be a protective factor for mood decline and QOL. Participants and Methods: The sample consisted of 47 adult patients (57.4% female; Age, M= 34.02(11.59)) with medically refractory epilepsy at the UCSF Epilepsy Center. Participants were included if they received surgical treatment for their epilepsy (42.6% right anterior temporal lobectomy [ATL], 46.8% left ATL, 2.1% laser ablation, 6.4% responsive neurostimulation, 2.1% multiple surgical interventions) and received both a pre- and post-surgical neuropsychological evaluation. Most patients were right-handed (95.7% right). Mood and QOL were assessed from pre- and post-operative evaluations using the Beck Depression Inventory- Second Edition (BDI-II), Beck Anxiety Inventory (BAI), and Quality of Life in Epilepsy- 31 (QOLIE-31). Executive function was assessed using the Trail Making Test, and the Delis-Kaplan Executive Function Scale (D-KEFS) subtests Color-Word Interference (CW-I) and Verbal Fluency. Descriptive statistics were obtained for each of the measures listed. A paired sample t-test was conducted between time A and B to determine whether mood and QOL were significantly different. Two multiple regressions were conducted. One analysis for post-operative depression and QOL respectively with pre-operative EF. Results: At time A, both anxiety and depression were minimal (BDI M= 17.8, SD= 10.34; BAI M= 13; SD= 8.94). QOL was borderline clinically significant (QOLIE M= 37.46, SD= 9.74). Depression at time B was positively correlated with depression at time A (r[45]= 0.316, p=0.035). A paired sample t-test indicated that depression and QOL were significantly different at time A and time B (t[44]= 2.04, p= 0.047; t[31]= -3.34, p= 0.002), with improved scores post-operatively. Anxiety was not significantly different across time points (t[39]= 1.20, p=0.238). Multiple regression analyses indicated that pre-operative depression and EF did not predict post-operative depression (F(5,27)= 1.62, p= 0.189). Pre-operative EF (CW-I Inhibition-Switching), but not pre-operative depression, predicted post-operative QOL (F(4(24)= 3.13, p=.03, R2= .343). Conclusions: Results were somewhat discrepant from prior research in that depression and QOL improved post-surgically. Notably, while the observed change in depression was statistically significant it was not clinically significant according to literature (Doherty et al., 2021). Pre-surgical inhibitory control predicted QOL, illustrating that EF may serve as a protective factor post-surgically. The present study did not include a measure of seizure freedom classification post-operatively, therefore, future studies should investigate how seizure freedom classification impacts the relationship between mood, QOL, and cognitive 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".