An investigation into the psychosocial effects of the postictal state
Bibliographic record
Abstract
OBJECTIVE: To determine whether postictal cognitive and behavioral impairment (PCBI) is independently associated with specific aspects of a patient's psychosocial health in those with epilepsy and nonepileptic events. METHODS: We used the University of Calgary's Comprehensive Epilepsy Clinic prospective cohort database to identify patients reporting PCBI. The cohort was stratified into those diagnosed with epilepsy or nonepileptic events at first clinic visit. Univariate comparisons and stepwise multiple logistic regression with backward elimination method were used to identify factors associated with PCBI for individuals with epilepsy and those with nonepileptic events. We then determined if PCBI was independently associated with depression and the use of social assistance when controlling for known risk factors. RESULTS: We identified 1,776 patients, of whom 1,510 (85%) had epilepsy and 235 had nonepileptic events (13%). PCBI was independently associated with depression in those with epilepsy (odds ratio [OR] 1.73; 95% confidence interval [CI] 1.06-2.83; p = 0.03) and with the need for social assistance in those with nonepileptic events (OR 4.81; 95% CI 2.02-11.42; p < 0.001). CONCLUSIONS: PCBI appears to be significantly associated with differing psychosocial outcomes depending on the patient's initial diagnosis. Although additional research is necessary to examine causality, our results suggest that depression and employment concerns appear to be particularly important factors for patients with PCBI and epilepsy and nonepileptic attacks, respectively.
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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 | 0.000 |
| 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".