Detecting depression in persons living with mild cognitive impairment: a systematic review
Bibliographic record
Abstract
OBJECTIVE: Depression is common in persons experiencing mild cognitive impairment (MCI), with 32% (95% Cl 27, 37) overall experiencing depression. Persons with MCI who have depression have more cognitive changes compared to those without depression. To understand how we can detect depressive symptoms in persons with MCI, we undertook a systematic review to identify tools that were validated compared with a reference standard. DESIGN: We searched MEDLINE, EMBASE, PsycINFO, and Cochrane from inception to April 25, 2021, and conducted a gray literature search. Title/abstract and full-text screening were completed in duplicate. Demographic information, reference standards, prevalence, and diagnostic accuracy measures were then extracted from included articles (PROSPERO CRD: CRD42016052120). RESULTS: Across databases, 8,748 abstracts were generated after removing duplicates. Six hundred and sixty-five records underwent full-text screening, with six articles included for data extraction. Nine tools were identified compared to a reference standard, with multiple demonstrating a sensitivity of 100% (Brief Assessment Schedule Depression Cards, Beck Depression Inventory-II, Cornell Scale for Depression in Dementia, Zung Self-Rated Depression Scale, and the Neuropsychiatric Inventory). The second highest sensitivity reported was 89% (Patient Health Questionnaire-9). Too few studies were available for a meta-analysis. CONCLUSIONS: Multiple depression detection tools have been examined amongst MCI outpatients, with several showing high sensitivity. However, this evidence is only present in single studies, with little demonstration of how differing MCI types affect accuracy. More research is needed to confirm the accuracy of these tools amongst persons with MCI. At this time, several tools could be suitable for use in cognitive clinics.
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".