Elderly suicide attempters: characteristics and outcome
Bibliographic record
Abstract
PURPOSE: We made a descriptive survey to assess the outcome of elderly patients discharged from a hospital psychiatric service after a suicide attempt (rates of overall mortality and repeat attempts), to identify the factors that had a significant impact on their survival and to determine patient characteristics. METHODS: Fifty-nine suicide attempters over 60 years of age admitted to hospital between 1993 and 2000 were included in the study. Their outcome was assessed by questioning their attending physicians over the telephone. We traced 51 of the 59 patients; 8 were lost to follow-up. Statistical analysis (Log Rank tests, Cox model) was computed to determine which factors altered the overall survival and the survival without further attempt. The patients sociodemographic, medical and psychiatric characteristics were recorded from hospital patient files. RESULTS: Elderly suicide attempters showed an increased mortality from suicide and natural causes and the risk of a repeat attempt increased in female patients with memory disorders. The factors altering survival were advanced age, pre-existing physical disability, several co-existing physical illnesses, severe physical consequences of the suicide attempt, history of psychiatric illness other than depression, memory disorders and one previous suicide attempt. The elderly suicide attempter was most likely to be a widowed woman suffering from social isolation, loneliness and depression. CONCLUSION: Elderly suicide attempters remained both physically and mentally vulnerable after their attempt. A repeat act represents a turning point in personal life progression which it is essential to detect.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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".