Susceptibility to Undue Influence: The Role of the Medical Expert in Estate Litigation
Bibliographic record
Abstract
OBJECTIVES: Medical experts are increasingly asked to assist the courts with Will challenges based on the determination of testamentary capacity and potential undue influence. Unlike testamentary capacity, the determination of undue influence has been relatively neglected in the medical literature. We aim to improve the understanding of the medical expert role in providing the courts with an opinion on susceptibility to undue influence in estate litigation. METHOD: Medical experts with experience in the assessment of testamentary capacity and susceptibility to undue influence collaborated with experienced estate litigators. The medical literature on undue influence was reviewed and integrated. The lawyers provided a historical background and a legal perspective on undue influence in estate litigation and the medical experts provided a clinical perspective on the determination of susceptibility to undue influence. Together, they provided recommendations for how the medical expert could best assist the court. RESULTS: Susceptibility to undue influence is frequently used in estate litigation to challenge the validity of Wills and is defined as subversion of the testator's free will by an influencer, resulting in changes to the distribution of the estate. While a determination of undue influence includes the documentation of indicia or suspicious circumstances under which the Will was drafted and executed, medical experts should focus primarily on the susceptibility of the testator to undue influence. This susceptibility should be based on a consideration of cognitive function, psychiatric symptoms, physical and behavioural function, with evidence derived from the medical documentation, the medical examination, and the history. CONCLUSIONS: The determination of undue influence is a legal one, but medical experts can help the court achieve the most informed legal decision by providing relevant information on clinical issues that may impact the testator's susceptibility to undue influence.
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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 | 0.028 | 0.120 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".