Differential neuropsychological test sensitivity to left temporal lobe epilepsy
Bibliographic record
Abstract
We examined the sensitivity of the Rey Auditory Verbal Learning Test (AVLT), California Verbal Learning Test (CVLT), Boston Naming Test (BNT), and Multilingual Aphasia Examination Visual Naming subtest (MAE VN) to lateralized temporal lobe epilepsy (TLE) in patients who subsequently underwent anterior temporal lobectomy. For the AVLT (n = 189), left TLE patients performed more poorly than their right TLE counterparts [left TLE = 42.9 (10.6), right TLE = 47.7 (9.9); p < .002 (Cohen's d = .47)]. Although statistically significant, the CVLT group difference (n = 212) was of a smaller magnitude [left LTE = 40.7 (11.1), right TLE = 43.8 (9.9); (p < .03, Cohen's d = .29)] than the AVLT. Group differences were also present for both measures of confrontation naming ability [BNT: left LTE = 43.1 (8.9), right TLE = 48.1 (8.9); p < .001 (Cohen's d = .56); MAE VN: left TLE = 42.2, right TLE = 45.6, p = .02 (Cohen's d = .36)]. When these data were modeled in independent logistic regression analyses, the AVLT and BNT both significantly predicted side of seizure focus, although the positive likelihood ratios were modest. In the subset of 108 patients receiving both BNT and AVLT, the AVLT was the only significant predictor of seizure laterality, suggesting individual patient variability regarding whether naming or memory testing may be more sensitive to lateralized TLE.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".