Telemedicine and Epilepsy Care - A Canada Wide Survey
Bibliographic record
Abstract
BACKGROUND: Canadian provinces boast one of the most sophisticated telemedicine infrastructures in the world. Feasibility of epilepsy care through telemedicine is established, but its use by practicing neurologists is unknown. The Canadian League against Epilepsy's telemedicine task force conducted this study to understand the perceptions, barriers and usage of telemedicine in epilepsy care. METHODS: Using a 14 item questionnaire we prospectively surveyed all the epileptologists across Canada with regards to current use, perceived benefits and barriers to the use of telemedicine. The survey was mailed out to 76 neurologists who had a primary interest in epilepsy. RESULTS: We received 39 responses (54.1%) spanning seven provinces. Majority of the responders were 50 years and over (56.4%). Although 61.5% of the physicians acknowledged a need for tele-epilepsy services, the majority (64.1%) had not used telemedicine. The most common forms of technology were videoconferencing and telephone but some physicians had also used email. Telemedicine was mainly used for clinical and educational purposes. 79.5% of physicians had access to videoconferencing equipment and 61.5% assessed that there was a need/use for clinical telehealth. The main perceived obstacles in the use of telemedicine were: lack of infrastructure support and remuneration problems followed by limitations in clinical examination. CONCLUSIONS: Although widely available, telemedicine is under-utilized in epilepsy care. Most of the obstacles can be easily fixed and overcome through education and simple interventions. Partnering of epilepsy centers across Canada in the development of a comprehensive national telemedicine network would create an excellent opportunity to expand epilepsy care.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".