Predictors of Unfavourable Seizure Outcome in Patients with Epilepsy in Nepal
Bibliographic record
Abstract
BACKGROUND: Despite optimal medical therapy, a sizeable number of patients continue to have persistent seizures. We evaluated the association of pretreatment and treatment variables with unfavorable seizure outcome. METHODS: Patients with follow-up over 12 years in the Nepal Epilepsy Association were evaluated. Patients having seizures for at least a year and already on polytherapy after failure of two monotherapy trials were considered having unfavourable outcome. Variables under study were: age, sex, duration and frequency of seizures prior to treatment, type of seizure, neurological status, Computed Tomography (CT) finding, and failure of first anti-epileptic drug (AED). Bivariate analysis was done with Chi-square and Fisher exact tests. Potential interaction between variables was studied with a logistic regression analysis. RESULTS: Out of a total 529 consecutive patients, 490 were included in the study. Unfavorable seizure outcome was seen in 26.8% of patients. Among 284 patients who remained viable for analysis, bivariate analysis showed significant association of unfavorable outcome with frequency of seizure (p 0.01), abnormal neurological status (p 0.01) and failure of first AED (p 0.00), while no significant association was seen with age at onset (p 0.45), sex (p 0.47), duration of seizure (p 0.43), type of seizure (p 0.12), and presence of CT abnormality (p 0.46). The fitted regression model portended an unfavorable prognosis with failure of first AED and abnormal neurological status, however, failed to show significant association with frequency of seizure. CONCLUSIONS: Failure of first AED trial and associated neurological deficits are significant predictors of unfavorable seizure outcome.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".