Predictors of meaningful improvement in quality of life after temporal lobe epilepsy surgery: A prospective study
Bibliographic record
Abstract
OBJECTIVES: To investigate prospectively the independent predictors of a minimum clinically important change (MCIC) in quality of life (QOL) after anterior temporal lobectomy (ATL) for drug-resistant mesial temporal lobe epilepsy related to hippocampal sclerosis (MTLE-HS) in Brazilian patients. METHODS: Multiple binary logistic regression analysis was performed to identify the clinical, demographic, radiologic, and electrophysiologic variables independently associated with MCIC in the Quality of Life in Epilepsy-31 Inventory (QOLIE-31) overall score 1 year after ATL in 77 consecutive patients with unilateral MTLE-HS. RESULTS: The overall QOLIE-31 score and all its subscale scores increased significantly (p < 0.0001) 1 year after ATL. In the final logistic regression model, absence of presurgical diagnosis of depression (adjusted odds ratio [OR] 4.4, 95% confidence interval [CI] 1.1-16.1, p = 0.02) and a complete postoperative seizure control (adjusted OR 4.1, 95% CI 1.2-14.5, p = 0.03) were independently associated with improvement equal to or greater than the MCIC in QOL after ATL. The overall model accuracy for MCIC improvement in the QOL was 85.6%, with a 95.2% of sensitivity and 46.7% of specificity. SIGNIFICANCE: These results in Brazilian patients reinforce the external validation of previous findings in Canadian patients showing that presurgical depression and complete seizure control after surgery are independent predictors for meaningful improvement in QOL after ATL, and have implications for the surgical management of MTLE patients.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".