“Younger” and “Older” Old Adults Who Die by Suicide: A Comparison Study and Cluster Analysis
Bibliographic record
Abstract
OBJECTIVES: To compare sociodemographic, clinical, and suicide-specific characteristics between younger old (aged 65-74) and older old (aged 75+) adults who died by suicide in 2015, and to identify clinically relevant subgroups in the entire study cohort 65+ through cluster analysis. DESIGN: Retrospective cohort study. SETTING: All health care units across Sweden. PARTICIPANTS: Individuals aged 65+ with at least one physician contact in the year preceding suicide (N = 277; aged 65-74 n = 145, 75+ n = 132). MEASUREMENTS: Variables retrieved from medical records and the Swedish Cause of Death Register. RESULTS: There were no differences between age groups, except being widowed, which was documented to a higher proportion in those aged 75+ (10% vs 28%, p < 0.001). Nearly half of the total cohort had a prescription for antidepressants at the time of death, but increased suicide risk was noted in only 13%. Two groups emerged via cluster analysis. One large group (n = 197) was characterized by male sex and comparatively low proportions with notations related to mental ill-health. The other group (n = 80) was characterized by high rates of mental illness including suicidal ideation and prescribed psychoactive medication. CONCLUSIONS: We identified no clinically meaningful age group differences. Cluster analysis revealed a large, predominantly male group in which one third were prescribed antidepressants. Otherwise, there was little documentation related to mental health, suggesting other suicidal precipitants, underdiagnosis, and/or underestimation of the severity of mental illness in that group. This points to a need for data sources that go beyond medical records to inform targeted prevention efforts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".