Depression screening tools in persons with epilepsy: A systematic review of validated tools
Bibliographic record
Abstract
OBJECTIVE: Depression affects approximately 25% of epilepsy patients. However, the optimal tool to screen for depression in epilepsy has not been definitively established. The purpose of this study was to systematically review the literature on the validity of depression-screening tools in epilepsy. METHODS: MEDLINE, EMBASE, and PsycINFO were searched until April 4, 2016 with no restriction on dates. Abstract, full-text review and data abstraction were conducted in duplicate. We included studies that evaluated the validity of depression-screening tools and reported measures of diagnostic accuracy (e.g., sensitivity, specificity, and negative and positive predictive values) in epilepsy. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies Version 2. Medians and ranges for estimates of diagnostic accuracy were calculated when appropriate. RESULTS: A total of 16,070 abstracts were screened, and 38 articles met eligibility criteria. Sixteen screening tools were validated in 13 languages. The most commonly validated screening tool was the Neurological Disorders Depression Inventory for Epilepsy (NDDI-E) (n = 26). The Mini International Neuropsychiatric Interview (MINI) (n = 19) was the most common reference standard used. At the most common cutpoint of >15 (n = 12 studies), the NDDI-E had a median sensitivity of 80.5% (range 64.0-100.0) and specificity of 86.2 (range 81.0-95.6). Meta-analyses were not possible due to variability in cutpoints assessed, reference standards used, and lack of confidence intervals reported. SIGNIFICANCE: A number of studies validated depression screening tools; however, estimates of diagnostic accuracy were inconsistently reported. The validity of scales in practice may have been overestimated, as cutpoints were often selected post hoc based on the study sample. The NDDI-E, which performed well, was the most commonly validated screening tool, is free to the public, and is validated in multiple languages and is easy to administer, although selection of the best tool may vary depending on the setting and available resources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".