Suicidal behavior and all‐cause mortality in depressed older adults aged 75+ treated with electroconvulsive therapy: A Swedish register‐based comparison study
Bibliographic record
Abstract
OBJECTIVES: Electroconvulsive therapy (ECT) is effective in treating late-life depression. There is limited research on suicidal behavior and all-cause mortality in the oldest old after ECT. METHODS: Older adults aged 75 years and above who had been inpatients for moderate to severe depression between January 1, 2011, and December 31, 2017, were included in the study. We used exact and propensity score matching to balance groups. We compared suicidal behavior (fatal and non-fatal) and all-cause mortality in those who had received ECT and those with other depression treatments. RESULTS: Of the study population, 1802 persons who received ECT were matched to 4457 persons with other treatments. There were no significant differences in the risk of suicidal behavior between groups, (within 3 months: odds ratio 0.73; 95% confidence intervals (CI), 0.44-1.23, within 4 months to 1 year: aOR 1.34; 95% CI, 0.84-2.13). All-cause mortality was lower among ECT recipients compared to those who had received other treatments, both within 3 months (aOR, 0.35; 95% CI, 0.23-0.52), and within 4 months to 1 year (aOR 0.65; 95% CI, 0.50-0.83). CONCLUSIONS: Compared to other depression treatments, ECT is not associated with a higher risk of suicidal behavior in patients aged 75 and above. ECT is associated with lower all-cause mortality in this age group, but we advise caution regarding causal inferences.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".