Nomograms to Predict Verbal Memory Decline After Temporal Lobe Resection in Adults With Epilepsy
Bibliographic record
Abstract
OBJECTIVE: To develop and externally validate models to predict the probability of postoperative verbal memory decline in adults after temporal lobe resection (TLR) for epilepsy using easily accessible preoperative clinical predictors. METHODS: Multivariable models were developed to predict delayed verbal memory outcome on 3 commonly used measures: Rey Auditory Verbal Learning Test (RAVLT) and Logical Memory (LM) and Verbal Paired Associates (VPA) subtests from Wechsler Memory Scale-Third Edition. With the use of the Harrell step-down procedure for variable selection, models were developed in 359 adults who underwent TLR at the Cleveland Clinic and validated in 290 adults at 1 of 5 epilepsy surgery centers in the United States or Canada. RESULTS: Twenty-nine percent of the development cohort and 26% of the validation cohort demonstrated significant decline on at least 1 verbal memory measure. Initial models had good to excellent predictive accuracy (calibration [c] statistic range 0.77-0.80) in identifying patients with memory decline; however, models slightly underestimated decline in the validation cohort. Model coefficients were updated with data from both cohorts to improve stability. The model for RAVLT included surgery side, baseline memory score, and hippocampal resection. The models for LM and VPA included surgery side, baseline score, and education. Updated model performance was good to excellent (RAVLT c = 0.81, LM c = 0.76, VPA c = 0.78). Model calibration was very good, indicating no systematic overestimation or underestimation of risk. CONCLUSIONS: Nomograms are provided in 2 easy-to-use formats to assist clinicians in estimating the probability of verbal memory decline in adults considering TLR for treatment of epilepsy. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that multivariable prediction models accurately predict verbal memory decline after TLR for epilepsy in adults.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".