Risk factors for comorbid epilepsy in patients with psychogenic non-epileptic seizures. Dataset of a large cohort study
Bibliographic record
Abstract
Psychogenic non-epileptic seizures (PNES) are the main differential diagnosis of pharmacorresistant epilepsy. Achieving the certainty in the diagnosis of PNES may be challenging, especially in the 10-22% of cases in which PNES and epilepsy co-exist. This difficulty hampers the management of these patients. Unfortunately, published series with this combined pathology are scarce and small in size. This article presents the dataset of our article "Factors associated with comorbid epilepsy in patients with psychogenic non-epileptic seizures: a large cohort study" (Massot-Tarrús et al. 2022). It is composed by a detailed demographic and clinical data of 271 consecutive patients diagnosed with PNES in our epilepsy monitoring unit (EMU) between May 2001 and February 2011, and followed until September 2016. Based on the clinical, neuroimaging and vEEG findings, 47 of these patients were diagnosed with definite comorbid epilepsy, and 30 with possible or probable comorbid epilepsy. All data was collected retrospectively from chart review. The cohort is depicted by means of demographic variables; age at PNES onset; years with PNES; frequency of PNES; duration of longest PNES seizure; self-reported history of minor head trauma (not associated with an increased risk of epilepsy) immediately preceding the first PNES; history of substance abuse; past or present history of active suicidal ideation; neuropsychological evaluation with the Minnesota Multiphasic Personality Inventory test; number and nature of risk factors for epilepsy; co-morbid degenerative brain disease or other neurological or psychiatric medical conditions; semiology of the seizures and triggers; EEG findings; type of epilepsy; number of past EMU admissions and epilepsy clinic visits and re-referrals; number of Anti-Seizure Medications (ASM) at EMU admission and discharge; and the outcome of the spells and ASM after the EMU discharge. Those ASM prescribed for reasons other than the treatment of the seizures (e.g., psychiatric disorders, migraine, pain syndromes, etc.) were not counted. The presented baseline data can be used in studies evaluating the characteristics of patients with PNES and comorbid epilepsy, and in the creation of algorithms to identify them. It could facilitate the prioritization of this subgroup of patients for prolonged video-EEG monitorization to confirm the co-existence of both types of seizures and treat them accordingly.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".