Testamentary capacity and delirium
Bibliographic record
Abstract
BACKGROUND: With the aging of the population there will be a substantial transfer of wealth in the next 25 years. The presence of delirium can complicate the evaluation of an older person's testamentary capacity and susceptibility to undue influence but has not been well examined in the existing literature. METHODS: A subcommittee of the IPA Task Force on Testamentary Capacity and Undue Influence undertook to review how to assess prospectively and retrospectively testamentary capacity and susceptibility to undue influence in patients with delirium. RESULTS: The subcommittee identified questions that should be asked in cases where someone changes their will or estate plan towards the end of their life in the presence of delirium. These questions include: was there consistency in the patient's wishes over time? Were these wishes expressed during a "lucid interval" when the person was less confused? Were the patient's wishes clearly expressed in response to open-ended questions? Is there clear documentation of the patient's mental status at the time of the discussion? CONCLUSIONS: This review with some case examples provides guidance on how to consider the question of testamentary capacity or susceptibility to undue influence in someone undergoing an episode of delirium.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".