Validating screening tools for depression in epilepsy
Bibliographic record
Abstract
OBJECTIVE: Depression is a common comorbidity of epilepsy, and its timely identification in persons with epilepsy is essential. The use of screening tools to detect depression is common in epilepsy, but some scales in current use have not been validated using a gold standard in this population. The present study aims to validate three commonly used depression-screening scales and assess new cut points for scoring in those with epilepsy. METHODS: Persons with epilepsy (n = 300) from the only epilepsy clinic in a large urban health region completed questionnaires (e.g., sociodemographics, adverse event profile) and three depression-screening tools (Hospital Anxiety and Depression Scale [HADS]; Patient Health Questionnaire [PHQ]-9 and PHQ-2). One hundred eighty-five patients participated in a gold-standard structured clinical interview to assess depression. The diagnostic accuracy of the depression scales was assessed comparing a variety of scoring cut points to the gold-standard diagnosis of depression. RESULTS: The prevalence of current depression in this population, according to the gold-standard, was 14.6%. The scale with the highest sensitivity (84.6%) was the HADS with a cut point of 6 and the scale with the highest specificity (96.2%) was the PHQ-9 algorithm scoring method. Overall, the PHQ-9 at a cut point of 9 and the HADS at a cut point of 7 resulted in the greatest balance of sensitivity and specificity (area under the curve: 88% and 90%, respectively). SIGNIFICANCE: The PHQ-9 at a cut point of 9 and the HADS at a cut point of 7 had the best overall balance of sensitivity and specificity. However, for screening purposes the PHQ-9 algorithm method is ideal (optimizing specificity), whereas for case finding the HADS at a cut point of 6 performed best (optimizing sensitivity). Appropriate scale cut points should be chosen based on the study's goals and available resources. Disease-specific scale cut points are recommended for future studies assessing depression in persons with epilepsy.
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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 | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".