Screening and Management of Depression and Anxiety in People With Epilepsy: A Quality Improvement Study
Bibliographic record
Abstract
BACKGROUND: Due to the high prevalence of depression and anxiety in people with epilepsy, the International League Against Epilepsy Commission on the Neuropsychiatric Aspects of Epilepsy recommends implementing routine screening for depression and anxiety symptoms. Our epilepsy group began administering three screening questionnaires to all clinic patients in 2016: the Neurological Disorders Depression Inventory for Epilepsy (NDDI-E), the Beck Anxiety Inventory (BAI) and the Generalized Anxiety Disorder-7 (GAD-7). OBJECTIVE: We aim to review our experience with this screening approach. METHODS: We reviewed 2253 sets of questionnaires completed from January 2018 to March 2020 and studied the actions taken by epileptologists in response to a positive screening. RESULTS: Thirty-six percent of all assessed patients screened positive on at least one questionnaire: 13.6% screened positive for depression symptoms (NDDI-E ≥ 16), 12.3% for anxiety symptoms (BAI ≥ 22) and 30.3% for GAD symptoms (GAD-7 > 7). Among patients with a positive screening, 36% received a care intervention, 59% did not and 5% declined the neurologist's recommendation. Among patients for whom an intervention was implemented, 58% were referred to a mental health professional (generally a neuropsychiatrist), 29% had their antiseizure medication adjusted to alleviate their symptoms and 13% received another intervention. CONCLUSION: In our clinic, an important proportion of patients screened positive for depression and/or anxiety symptoms. Fewer than half received a management option to alleviate their symptoms. We conclude that while routine screening increases the detection of depression and anxiety among epilepsy patients, it must be accompanied by effective interventions and access to mental-health professionals.
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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 | 0.040 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".