A curriculum for epilepsy surgery: A report from the Surgical Commission's Epilepsy Surgery Educational Task Force and the Educational Council of the <scp>ILAE</scp>
Bibliographic record
Abstract
OBJECTIVE: There is a need to develop a comprehensive, formalized, and globally applicable epilepsy surgery curriculum in order to help standardize the quality of epilepsy surgery practice in the world. METHODS: The Epilepsy Surgery Educational Task Force of the ILAE developed a competency-based epilepsy surgery educational curriculum comprised of four domains: Diagnosis, Counseling, Pre-surgical Work-up, and Surgical Techniques. To evaluate this educational curriculum, a survey questionnaire consisting of 11 items was sent out for feedback to ILAE and IESS members. The survey questionnaire asked respondents to rate the degree of importance of each competency on a five-point scale that ranged from "extremely important" to "not at all important" and provided space for free text comments. All competencies that were rated as "slightly important" or "somewhat important" by more than 10% of responders, as well as all free text feedback comments, were fully reviewed, and the final curriculum was adjusted accordingly. RESULTS: One hundred and twenty-two responses (n = 122) were submitted by the ILAE and IESS communities. Seventy-one percent (71.9%) of the responses came from neurosurgeons of varied experience and training levels. Ninety-four percent (94.4%) of responders stated that they "support a curriculum to determine the competency of neurosurgeons participating in the care of people with epilepsy in my country," and 94.3% confirmed that they "would be interested in taking or would recommend an ILAE course based on these competencies, that would provide an ILAE certificate of completion." Relevant changes within the curriculum were made based on feedback. SIGNIFICANCE: The newly developed ILAE comprehensive epilepsy surgery educational curriculum has a high level of support from neurosurgeons and neurologists around the world and shows potential for broad acceptance and implementation.
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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 | 0.022 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".