Suicide and assisted dying in dementia: what we know and what we need to know. A narrative literature review
Bibliographic record
Abstract
BACKGROUND: Evidence-based data on prevalence and risk factors of suicidal intentions and behavior in dementia are as scarce as the data on assisted dying. The present literature review aimed on summarizing the current knowledge and provides a critical discussion of the results. METHODS: A systematic narrative literature review was performed using Medline, Cochrane Library, EMBASE, PSYNDEX, PSYCINFO, Sowiport, and Social Sciences Citation Index literature. RESULTS: Dementia as a whole does not appear to be a risk factor for suicide completion. Nonetheless some subgroups of patients with dementia apparently have an increased risk for suicidal behavior, such as patients with psychiatric comorbidities (particularly depression) and of younger age. Furthermore, a recent diagnosis of dementia, semantic dementia, and previous suicide attempts most probably elevate the risk for suicidal intentions and behavior. The impact of other potential risk factors, such as patient's cognitive impairment profile, behavioral disturbances, social isolation, or a biomarker based presymptomatic diagnosis has not yet been investigated. Assisted dying in dementia is rare but numbers seem to increase in regions where it is legally permitted. CONCLUSION: Most studies that had investigated the prevalence and risk factors for suicide in dementia had significant methodological limitations. Large prospective studies need to be conducted in order to evaluate risk factors for suicide and assisted suicide in patients with dementia and persons with very early or presymptomatic diagnoses of dementia. In clinical practice, known risk factors for suicide should be assessed in a standardized way so that appropriate action can be taken when necessary.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".