44 Finding the Onramp: Understanding Access to Neuropsychological Evaluation in New Onset Pediatric Epilepsy
Bibliographic record
Abstract
Objective: Approximately half of all children and adults newly diagnosed with epilepsy also show behavioral and/or cognitive difficulties upon evaluation. While neuropsychological screening is recommended as a routine part of care at seizure onset, in reality, access to care is often restricted by many factors. In order to better define the extent of the problem, we developed a survey to understand how frequently youth with new onset epilepsy currently undergo neuropsychological evaluation or screening and whether virtual assessment tools are used to extend access to care. Participants and Methods: We created an online survey to better understand new onset epilepsy care provided within neuropsychological practice settings in the United States and Canada. The survey was disseminated via multiple listservs (e.g., AACN listservs, APPCN, PERF neuropsychologists) and respondents included 45 neuropsychologists. Survey questions were grouped by the following domains: 1) location characteristics (e.g., urban versus rural location, type of practice, affiliation with comprehensive epilepsy center); 2) volume of new onset epilepsy patient cases (e.g., number of neuropsychologists within practice who see new onset patients, percentage of new onset cases who received neuropsychological evaluations/screeners, wait time), and 3) tele-neuropsychology procedures (e.g., use of virtual testing, frequency of virtual testing, frequency of virtual intakes/feedbacks). Results: Practice locations of the 45 respondents included academic medical center (n=34, 75.6%), community medical center (n=10, 22.2%), and private practice (n=1, 2.2%). All but one respondent practiced in an urban setting. Respondents were generally affiliated with Comprehensive Epilepsy Centers (level 3 or 4) (n=39, 86.7%). Practice settings typically included < 3 epilepsy neuropsychologists (n=29, 65.9%). Of interest, neuropsychological evaluation of new onset pediatric epilepsy patients generally ranged from 0-25% of cases (n=32, 71%; mode=11-25%). Reported barriers included: insurance, poor access to rural populations, interdisciplinary communication, departmental referral patterns, limited number of providers, and need to prioritize pre-surgical patients. In terms of access, neuropsychology waitlist times for patients with nonsurgical epilepsy ranged from <1 to 6 months (n=34, 75%) with an equal proportion of patients waiting 1-3 months (33%) and 4-6 months (33%). Telehealth was not frequently utilized in non-surgical epilepsy test administration (Do not use, n=39; 86.7%), but frequently incorporated for non-testing purposes (i.e., intakes, feedbacks) (n=40, 88.9%). Conclusions: Results of this provider survey indicate that children with new-onset epilepsy do not routinely undergo neuropsychological evaluation (< 25%). Barriers included prioritizing presurgical workups, referral patterns, access to care, and limited provider bandwidth. Clearly, there is a need to improve access to care. Possible solutions include developing more time efficient screening batteries with measures most sensitive to early cognitive and psychosocial deficits, and incorporating the use of virtual technology all in the service of improving the lives of children with 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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".