An international pilot study of an internet‐based platform to facilitate clinical research in epilepsy: The EpiNet project
Bibliographic record
Abstract
PURPOSE: We created an epilepsy patient database that can be accessed via the Internet by neurologists from anywhere in the world. The database was designed to enroll and follow large cohorts of patients with specific epilepsy syndromes, and to facilitate recruitment of patients for investigator-initiated clinical trials. METHODS: The EpiNet database records physician-derived information regarding seizure type and frequency, epilepsy syndrome, etiology, drug history, and investigations. It can be accessed from any country by approved investigators via a secure, password-protected Website. All data are encrypted. The database is for both research and clinical purposes. Investigators were invited to register any patient with epilepsy, but were particularly encouraged to register patients when uncertain of the optimal management. Participation required approval from investigators' ethics committees and institutional review boards, and all patients or their caregiver provided written informed consent. Patients were not enrolled in clinical trials in this pilot study. KEY FINDINGS: The international pilot study recruited patients from September 2010 to November 2011. Sixty-four investigators or research assistants from 25 centers in 13 countries registered 1,050 patients. Patients with a wide range of epilepsy syndromes and etiologies were registered. Patients' ages ranged from 2 weeks to 90 years. SIGNIFICANCE: The Website was successfully used by doctors working in different health systems. The pilot study confirmed that this low-cost, collaborative approach to research has great potential. Large, multicenter cohort studies will commence in 2012, and randomized clinical trials are being planned. All epileptologists are invited to join this project.
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 | 0.050 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".