A short screen for depression: the AB Clinician Depression Screen (ABCDS)
Bibliographic record
Abstract
BACKGROUND: Depression is common in elderly people but physicians may not screen for it because of the length of time required by current screening instruments. We have developed a short screening instrument for depression for use in elderly people with normal cognition, mild cognitive impairment or early dementia. METHODS: Participants were aged 55 years or more, had scored 20 or more on the standardized Mini-mental State Examination (SMMSE) and had been referred to a specialist geriatric outpatient memory clinic. Scores on the 30-item Geriatric Depression Scale (GDS) were analyzed. A composite GDS score, consisting of the top five individual question scores that correlated to depression (GDS >or= 14), were analyzed using a receiver operating curve analysis. RESULTS: There were 810 patients with SMMSE scores of 20 or greater, of whom 202 (24.9%) scored 14 or more on the GDS, indicating depression. GDS question 16, "Do you often feel downhearted and blue?," had the highest correlation with the overall scores of 14 or more on the 30-point instrument (r = 0.64, p < 0.001). The next four questions with the highest correlates were Q10, "Do you often feel helpless?" (r = 0.56, p < 0.001), Q3, "Do you feel that your life is empty?" (r = 0.54, p < 0.001), Q9, "Do you feel happy most of the time?" (r = 0.52, p < 0.001), and Q1, "Are you basically satisfied with your life?" (r = 0.50, p < 0.001). The negative predictive value of "Do you often feel downhearted and blue?" answered negatively for depression was 96%. These five questions were used as a short screening instrument. The positive predictive value of four or five positive responses was 97%. These data were not significantly different whether the patient's SMMSE score was 20-25 or 26-30. CONCLUSIONS: The AB Clinician Depression Screen (ABCDS), comprising five questions, can rapidly identify patients with depression or eliminate that diagnosis. In this population, these five questions may be used instead of the longer 30-question GDS scale.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".