Establishment of a Comprehensive Epilepsy Center in Pakistan: Initial Experiences, Results, and Reflections
Bibliographic record
Abstract
Background. Developing countries, home to 80% of epilepsy patients, do not have comprehensive epilepsy surgery programs. Considering these needs we set up first epilepsy surgery center in Pakistan. Methods. Seventeen teleconferences focused on setting up an epilepsy center at the Aga Khan University (AKU), Karachi, Pakistan were arranged with experts from the University of Alberta Hospital, Alberta, Canada and the University of West Virginia, USA over a two-year period. Subsequently, the experts visited the proposed center to provide hands on training. During this period several interactive teaching sessions, a nationwide workshop, and various public awareness events were organized. Results. Sixteen patients underwent surgery, functional hemispherectomy (HS) was done in six, anterior temporal lobectomy (ATL) in six, and neuronavigation-guided selective amygdalohippocampectomy (SAH) using keyhole technique in four patients. Minimal morbidity was observed in ATL and, SAH groups. All patients in SAH group (100%) had Grade 1 control, while only 5 patients (83%) in ATL group, and 4 patients (66%) in HS group had Grade 1 control according to Engel's classification, in average followups of 12 months, 24 months and 48 months for SAH, ATL, and HS, respectively. Conclusion. As we share our experience we hope to set a practical example for economically constrained countries that successful epilepsy surgery centers can be managed with limited resources.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".