Explanatory factors of quality of life in psychogenic non-epileptic seizure
Bibliographic record
Abstract
OBJECTIVE: Previous studies showed that patients with Psychogenic Non-Epileptic Seizures (PNES) have poor quality of life (QoL). This study explored the explicative factors of the QoL at the time of diagnosis and monitored changes over the two years of follow-up. METHODS: We evaluated 107 participants with a diagnosis of Psychogenic Non-Epileptic Seizure (PNES), every 6 months for 24 months. Quality of life (QoL) was evaluated using the QOLIE-31 and SF-36 scales (respectively specific and generic scales of QoL). Positive evolution of QoL was defined by the increase in the score of overall QoL using QOLIE-31 sub-scale from baseline to the last interview of the patient. We also collected for each patient data on psychiatric dimensions (childhood abuse, history of traumatic events, post-traumatic stress disorder (PTSD), depression, anxiety, alexithymia, and dissociation), clinical evolution of seizures and the number of mental health consultations. RESULTS: According to the QOLIE-31 and the SF-36, depression (p ≤ 0.001), anxiety (p < 0.001), alexithymia (p ≤ 0.001), and dissociation (p ≤ 0.004) were related to QoL at the time of the diagnosis. According to SF-36 (mental and physical), PTSD was also significantly associated with QoL (p < 0.05). The number of seizures or the co-occurrence of epilepsy did not influence QoL. Positive evolution of QoL was linked to the number of consultations for mental health issues (p = 0.02). SIGNIFICANCE: Post-traumatic dimensions (PTSD, dissociation), alexithymia and psychiatric comorbidities (depression and anxiety disorders) seem to alter QoL in people with PNES. The current study suggests that mental health care improves QoL of patients with PNES.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".