Bridging the healthcare gap: Building the case for epilepsy virtual clinics in the current healthcare environment
Bibliographic record
Abstract
Access to quality healthcare remains a challenge that is complicated by mounting pressures to control costs, and now, as we witness, the unprecedented strain placed on our healthcare delivery systems due to the COVID-19 pandemic. Challenges in healthcare access have driven a need for innovative approaches ensuring connectivity to health providers. Telehealth services and virtual clinics offer accessible disease management pathways for patients living in health resource limited areas or, as in the case of the COVID-19 pandemic, where there may be potential barriers to existing healthcare resources. Those suffering with serious chronic disorders often cannot be seen by a healthcare specialist due to their limited availability, or the lack of a specialist within a reasonable proximity. Epilepsy represents such a disorder where most of the world's population lacks the availability of necessary specialists. Virtual clinics allow for specialist care and an ability to perform necessary ambulatory electroencephalogram (EEG) monitoring by placing the technologies directly in patients' homes or at local clinics near the patients' homes. By moving the diagnostic process out of the hospital or epilepsy center, it becomes possible to overcome growing gaps in neurology services. Virtual clinics have the potential to expand access to high-quality, cost-effective care for the patient. The virtual clinic remotely connects those in need of medical support with specialists anywhere in the world, at any time of the day.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".