Novel analogous tasks to assess material-specific memory impairments associated with temporal lobe epilepsy
Bibliographic record
Abstract
Episodic memory tasks employing verbal material are generally sensitive to material-specific memory impairments in individuals with left mesial temporal lobe epilepsy (LTLE), whereas visuospatial memory tasks are less consistently failed by individuals with right mesial temporal lobe epilepsy (RTLE). A limitation of these tasks is the possibility for the examinee to use verbalization strategies when tested with visuospatial stimuli, and visualization strategies with verbal material. In this study, we aimed to develop two new analogous computerized recognition tasks to identify material-specific memory impairments, adapted from those previously developed in our lab: one using verbal material (i.e., pseudowords), and the other visuospatial material (i.e., pictures depicting similar landscapes of mountains, trees, and lakes). Each task consists of a 3-trial learning phase and delayed recognition trials after 30 min. and two weeks. After having established normative data for adults (N = 124), we assessed the ability of each task to detect material-specific memory impairments in patients who have had surgery for LTLE (n = 16) or RTLE (n = 12). Both tasks have good psychometric properties. The RTLE group showed significantly poorer performance on the visuospatial than on the verbal memory test on all trials. The LTLE group showed significantly poorer performance on the verbal than on the visuospatial memory test on delayed (30-min. and 2-week) recognition trials. Memory profile on delayed recognition trials was concordant with the lateralization of epilepsy surgery in 87.5% of LTLE and in 83.3% of the RTLE group. This study provides preliminary clinical validation for our novel tasks to detect material-specific memory impairments in individuals with temporal lobe epilepsy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".