Medical and surgical treatment of epilepsy in older adults: A national survey
Bibliographic record
Abstract
OBJECTIVE: There are no clinical guidelines dedicated to the treatment of epilepsy in older adults. We investigated physician opinion and practice regarding the treatment of people with epilepsy aged 65 years or older. We also sought to study how our opinion and practice varied between geriatricians, general neurologists, and epilepsy neurologists (i.e., epileptologists). METHODS: We initially piloted our survey to measure test-retest reliability. Once finalized, we disseminated the survey via two rounds of facsimiles, and then conventional mail, to eligible individuals listed in a national directory of Canadian physicians. We used descriptive statistics such as stacked bar charts and tables to illustrate our findings. RESULTS: One hundred forty-four physicians (104 general neurologists, 25 geriatricians, and 15 epileptologists) answered our survey in its entirety (overall response rate of 13.2%). Levetiracetam and lamotrigine were the preferred antiseizure medications (ASMs) to treat older adults with epilepsy. Two thirds of epileptologists and almost half of general neurologists would consider prescribing lacosamide in >50% of people aged >65 years; only one geriatrician was of the same opinion. More than 40% of general neurologists and geriatricians erroneously believed that none of the ASMs mentioned in our survey was previously studied in randomized controlled trials specific to the treatment of epilepsy in older adults. Epileptologists were more likely as compared to general neurologists and geriatricians to recommend epilepsy surgery (e.g., 66.6% vs. 22.9%-37.5% among older adults). SIGNIFICANCE: Therapeutic decisions for older adults with epilepsy are heterogeneous between physician groups and sometimes misalign with available clinical evidence. Our surveyed physicians differed in their approach to ASM choice as well as perception of surgery in older adults with epilepsy. These findings likely reflect the lack of clinical guidelines dedicated to this population and the deficient implementation of best practices.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".