Postsurgical Outcomes of Mesial Temporal Lobe Epilepsy due to Hippocampal Sclerosis Associated with Calcified Neurocysticercosis
Bibliographic record
Abstract
The aim of this study was to analyze postsurgical outcomes for individuals with mesial temporal lobe epilepsy with hippocampal sclerosis (MTLE-HS) who underwent anterior temporal lobectomy, based on the presence of calcified neurocysticercosis (cNCC). A retrospective cross-sectional study was conducted on 89 patients with MTLE-HS who underwent anterior temporal lobectomy between January 2012 and December 2020 at a basic epilepsy surgery center located in Lima, Peru. We collected sociodemographic, clinical, and diagnostic information. The postsurgical results were analyzed using bivariate analysis according to the Engel classification. We included 89 individuals with a median age of 28 years (interquartile range [IQR]: 24-37), and more than half (55.1%) were male. Seventeen (19.1%) were diagnosed with cNCC. A greater number of patients with cNCC had lived in rural areas of Peru during their early life compared with those without cNCC (12 [70.6%] versus 26 [36.1%]; P = 0.010). Patients with cNCC exhibited a greater median frequency of focal to bilateral tonic-clonic seizures per month (1 [IQR: 0-2] versus 0 [0-0.5]; P = 0.009). Conversely, a lower proportion of patients with cNCC reported a history of an initial precipitating injury in comparison to the group without cNCC (4 [23.5%] versus 42 [58.3%]; P = 0.014). At the 1-year follow-up, most patients (82.4%) with cNCC were categorized as Engel IA. Similarly, at the 2-year follow-up, nine (75.0%) were classified as Engel IA. Our findings suggest that most patients diagnosed with cNCC exhibit favorable postsurgical outcomes, comparable to those without cNCC. Additionally, it can be postulated that cNCC may play a role as an initial precipitating injury.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".