Screening for Suicide Ideation among Older Primary Care Patients
Bibliographic record
Abstract
OBJECTIVES: Older adults have high rates of suicide and typically seek care in primary medical practices. Older adults often do not directly or spontaneously report thoughts of suicide, which can impede suicide prevention efforts. Therefore, the use of additional approaches to suicide risk detection is needed, including the use of screening tools. The objective of this study was to assess whether brief screens for depression have acceptable operating characteristics in identifying suicide ideation among older primary care patients and to examine potential sex differences in the screen's accuracy. METHODS: We administered the 15-item Geriatric Depression Scale (GDS), which includes a 5-item GDS subscale (GDS-SI) designed to screen for suicide ideation, to a cross-sectional cohort of 626 primary care patients (235 men, 391 women) 65 years of age or older in the Northeastern United States. We assessed presence of suicide ideation with items from the Hamilton Rating Scale for Depression and the Structured Clinical Interview for the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition. RESULTS: Patients expressing suicide ideation (n = 69) scored higher on the GDS and GDS-SI than those who did not (n = 557). A GDS cut score of 4 maximized sensitivity (0.754) and specificity (0.815), producing an area under the curve of 0.844 (P < .001) and positive and negative predictive values of 0.335 and 0.964, respectively. Optimal cut scores were 5 for men and 3 for women. A GDS-SI cut score of 1 was optimal for the total sample and for both men and women. CONCLUSIONS: The GDS and GDS-SI accurately identify older patients with suicide ideation. Research is needed to examine their acceptability and barriers to routine use in primary care.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".