Clinical recommendations for conducting pediatric functional language and memory mapping during the phase I epilepsy presurgical workup
Bibliographic record
Abstract
Objective: Pediatric epilepsy surgery effectively controls seizures but may risk cognitive, language, or memory decline. Historically, the intra-carotid anesthetic procedure (IAP or Wada Test) was pivotal for language and memory function. However, advancements in noninvasive mapping, notably functional magnetic resonance imaging (fMRI), have transformed clinical practice, reducing IAP’s role in presurgical evaluations. Method: We conducted a critical narrative review on mapping technologies, including factors to consider for discordance. Results: Neuropsychological findings suggest that if pre-surgery function remains intact and the surgery targets the eloquent cortex, there is a high chance for decline. Memory and language decline are particularly pronounced post-left anterior temporal lobe resection (ATL), making presurgical cognitive assessment crucial for predicting postoperative outcomes. However, the risk of functional decline is not always clear – particularly with higher rates of atypical organization in pediatric epilepsy patients and discordant findings from cognitive mapping. We found little research to date on the use of IAP and other newer technologies for lateralization/localization in pediatric epilepsy. Based on this review, we introduce an IAP decision tree to systematically navigate discordance in IAP decisions for epilepsy presurgical workup. Conclusions: Future research should be aimed at pediatric populations to improve the precision of functional mapping, determine which methods predict post-surgical deficits and then create evidence-based practice guidelines to standardize mapping procedures. Explicit directives are needed for resolving conflicts between developing mapping procedures and established clinical measures. The proposed decision tree is the first step to standardize when to consider IAP or invasive mapping, in coordination with the multidisciplinary epilepsy surgical team.
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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 | 0.012 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.022 | 0.010 |
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".