Race/Ethnicity, Sex, and Socioeconomic Status as Predictors of Outcome After Surgery for Temporal Lobe Epilepsy
Bibliographic record
Abstract
BACKGROUND: Several risk factors have been attributed to seizure recurrence after surgery. It is unknown whether race/ethnicity plays a role in outcome. OBJECTIVE: To evaluate whether race/ethnicity plays a role in seizure recurrence after surgery. DESIGN: Cohort study. SETTING: We evaluated data obtained from the epilepsy centers at the University of Alabama at Birmingham and New York University, New York, NY. PATIENTS: All patients included had a diagnosis of mesial temporal sclerosis and underwent temporal lobectomy. MAIN OUTCOME MEASURES: Occurrence of seizure after surgery was registered 1 year after surgery. We used multiple logistic regression analysis to model the presence of seizure recurrence after surgery and generated odds ratios (ORs) for seizure recurrence after surgery for African American and Hispanic patients relative to white patients. An unadjusted model incorporated only race/ethnicity as the independent variable, and an adjusted model included socioeconomic status, age, duration of epilepsy, education, history of febrile seizures, sex, handedness, lateralization of epileptogenic focus, and number of antiepileptics as the independent variables. RESULTS: Two hundred fifty-two patients underwent surgical treatment with pathological confirmation of mesial temporal sclerosis. No differences were found between racial/ethnic groups in terms of seizure recurrence in any models. For African American patients, the ORs were 0.9 (95% confidence interval [CI], 0.4-2.1) for the unadjusted model and 0.8 (95% CI, 0.3-2.0) for the adjusted model; for Hispanic patients, the ORs were 1.6 (95% CI, 0.8-3.2) for the unadjusted model and 1.1 (95% CI, 0.5-2.6) for the adjusted model, relative to white patients. CONCLUSION: Our data suggest that although sex appears to play a role in the outcomes of surgery for temporal lobe epilepsy, race and socioeconomic status do not.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".