Stereoelectroencephalographic language mapping of the basal temporal cortex predicts postoperative naming outcome
Bibliographic record
Abstract
OBJECTIVE: In drug-resistant temporal lobe epilepsy (TLE) patients, the authors evaluated early and late outcomes for decline in visual object naming after dominant temporal lobe resection (TLR) according to the resection status of the basal temporal language area (BTLA) identified by cortical stimulation during stereoelectroencephalography (SEEG). METHODS: Twenty patients who underwent SEEG for drug-resistant TLE met the inclusion criteria. During language mapping, a site was considered positive when stimulation of two contiguous contacts elicited at least one naming impairment during two remote sessions. After TLR ipsilateral to their BTLA, patients were classified as BTLA+ when at least one positive language site was resected and as BTLA- when all positive language sites were preserved. Outcomes in naming and verbal fluency tests were assessed using pre- and postoperative (means of 7 and 25 months after surgery) scores at the group level and reliable change indices (RCIs) for clinically meaningful changes at the individual level. RESULTS: BTLA+ patients (n = 7) had significantly worse naming scores than BTLA- patients (n = 13) within 1 year after surgery but not at the long-term evaluation. No difference in verbal fluency tests was observed. When RCIs were used, 5 of 18 patients (28%) had naming decline within 1 year postoperatively (corresponding to 57% of BTLA+ and 9% of BTLA- patients). A significant correlation was found between BTLA resection and naming decline. CONCLUSIONS: BTLA resection is associated with a specific and early naming decline. Even if this decline is transient, naming scores in BTLA+ patients tend to remain lower compared to their baseline. SEEG mapping helps to predict postoperative language outcome after dominant TLR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".