Rapid Screening for Perceived Cognitive Impairment in Major Depressive Disorder
Bibliographic record
Abstract
BACKGROUND: Subjectively experienced cognitive impairment is common in patients with mood disorders. The British Columbia Cognitive Complaints Inventory (BC-CCI) is a 6-item scale that measures perceived cognitive problems. The purpose of this study is to examine the reliability of the scale in healthy volunteers and depressed patients and to evaluate the sensitivity of the measure to perceived cognitive problems in depression. METHODS: Participants were 62 physician-diagnosed inpatients or outpatients with depression, who had independently confirmed diagnoses on the Structured Clinical Interview for DSM-IV, and a large sample of healthy community volunteers (n=112). RESULTS: The internal consistency reliability of the BC-CCI was α=.86 for patients with depression and α=.82 for healthy controls. Principal components analyses revealed a one-factor solution accounting for 54% of the total variability in the control sample and a 2-factor solution (cognitive impairment and difficulty with expressive language) accounting for 76% of the variance in the depression sample. The total score difference between the groups was very large (Cohen's d=2.2). CONCLUSIONS: The BC-CCI has high internal consistency in both depressed patients and community controls, despite its small number of items. The test is sensitive to cognitive complaints in patients with depression.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".