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Record W2093568706 · doi:10.1017/s1041610210000712

Testamentary capacity and delirium

2010· review· en· W2093568706 on OpenAlexaff
Benjamin Liptzin, Carmelle Peisah, Kenneth I. Shulman, Sanford I. Finkel

Bibliographic record

VenueInternational Psychogeriatrics · 2010
Typereview
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsTestamentary trustDeliriumUndue influenceEstate planningMedicineConsistency (knowledge bases)Mental capacityEstatePsychologyPopulationPsychiatryPolitical scienceLawComputer scienceEnvironmental health

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.958
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.356
Teacher spread0.318 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations23
Published2010
Admission routes1
Has abstractyes

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