Effects of Epilepsy on Language Functions: Scoping Review and Data Mining Findings
Bibliographic record
Abstract
Purpose: This study involved a scoping review to identify possible gaps in the empirical description of language functioning in epilepsy in adults. With access to social network data, data mining was used to determine if individuals with epilepsy are expressing language-related concerns. Method: For the scoping review, scientific databases were explored to identify pertinent articles. Findings regarding the nature of epilepsy etiologies, patient characteristics, tested language modalities, and language measures were compiled. Data mining focused on social network databases to obtain a set of relevant language-related posts. Results: The search yielded 66 articles. Epilepsy etiologies except temporal lobe epilepsy and older adults were underrepresented. Most studies utilized aphasia tests and primarily assessed single-word productions; few studies included healthy control groups. Data mining revealed several posts regarding epilepsy-related language problems, including word retrieval, reading, writing, verbal memory difficulties, and negative effects of epilepsy treatment on language. Conclusion: Our findings underscore the need for future specification of the integrity of language in epilepsy, particularly with respect to discourse and high-level language abilities. Increased awareness of epilepsy-related language issues and understanding the patients' perspectives about their language concerns will allow researchers and speech-language pathologists to utilize appropriate assessments and improve quality of care.
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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 | 0.045 | 0.218 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.047 | 0.037 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".