Psychiatric outcomes of epilepsy surgery: A systematic review
Bibliographic record
Abstract
PURPOSE: The objective of this systematic review was to identify: (1) prevalence and severity of psychiatric conditions before and after resective epilepsy surgery, (2) incidence of postsurgical psychiatric conditions, and (3) predictors of psychiatric status after surgery. METHODS: A literature search was conducted using PubMed, EmBase, and the Cochrane database as part of a larger project on the development of an appropriateness and necessity rating tool to identify patients of all ages with potentially resectable focal epilepsy. The search yielded 5,061 articles related to epilepsy surgery and of the 763 articles meeting the inclusion criteria and reviewed in full text, 68 reported psychiatric outcomes. Thirteen articles met the final eligibility criteria. KEY FINDINGS: The studies demonstrated either improvements in psychiatric outcome postsurgery or no changes in psychiatric outcome. Only one study demonstrated deterioration in psychiatric status after surgery, with higher anxiety in the context of continued seizures post-surgery. One study reported a significantly increased rate of psychosis after surgery. The two main predictors of psychiatric outcome were seizure freedom and presurgical psychiatric history. De novo psychiatric conditions occurred postsurgery at a rate of 1.1-18.2%, with milder psychiatric issues (e.g., adjustment disorder) being more common than more severe psychiatric issues (e.g., psychosis). SIGNIFICANCE: Overall, studies demonstrated either improvement in psychiatric outcomes postsurgery or no change. However, there is a need for more prospective, well-controlled studies to better delineate the prevalence and severity of psychiatric conditions occurring in the context of epilepsy surgery, and to identify specific predictors of psychiatric outcomes 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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".