Quality of Life in Psychogenic Nonepileptic Seizures
Bibliographic record
Abstract
PURPOSE: Psychogenic nonepileptic seizures (PNESs) are events that alter or seem to alter the neurologic function and, in their appearance, resemble epileptic seizures (ESs). In patients with ESs the psychological and medical aspects of epilepsy greatly influence the health-related quality of life (HRQOL). The relation between these factors and PNESs is not well established. In this study, we compared HRQOL in patients with PNESs with that of patients with ESs. METHODS: We evaluated 105 patients admitted to the Epilepsy Monitoring Unit of University Hospital between January 20, 2001, and January 20, 2002. Only patients with the definite diagnosis of ESs or PNESs were analyzed (n = 85). Patients completed an epilepsy-specific quality-of-life instrument (QOLIE-89), the Profile of Mood States (POMS), and Adverse Events Profile (AEP). We used t tests and regression analyses to contrast HRQOL in PNESs and ESs and to elucidate the main factors associated with HRQOL in patients with PNESs. RESULTS: In our sample, 45 patients had PNESs, and 40 had ESs. The overall HRQOL and scores on 13 of 19 QOLIE-89 subscales were significantly lower (i.e., worse) in PNES than in ES patients. AEP and scores on five of six POMS subscales also were worse in PNES patients than in ES patients. PNES versus ES diagnosis, POMS depression/dejection, and AEP were significant predictors of HRQOL, jointly explaining 65% variation in HRQOL. The lower HRQOL in PNESs versus ESs was in part explained by depression and AEP. CONCLUSIONS: Patients with PNESs have a lower HRQOL and worse mood problems than do patients with ESs. This disadvantage is primarily due to depression and medication side effects, although these factors influence QOL in much the same way in PNES and ES patients. These baseline HRQOL data on patients with PNESs can be used to evaluate the effects of treatment in this patient population.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".