Assessing and managing patients with borderline personality disorder requesting medical assistance in dying
Bibliographic record
Abstract
Background: When physician assisted dying (referred to as Medical Assistance in Dying or MAiD in this article) is available for individuals with mental disorders as the sole underlying medical condition (MD-SUMC), patients with borderline personality disorder (BPD) frequently request MAiD. Psychiatrists and other clinicians must be prepared to evaluate and manage these requests. Objectives: The purposes of this paper are to define when patients with BPD should be considered to have an irremediable, treatment resistant disorder and provide clinicians with an approach to assess and manage their patients with BPD making requests for MAiD. Methods: This perspective paper developed the authors' viewpoint by using a published, authoritative definition of irremediability and including noteworthy systematic and/or meta-analytic reviews related to the assessment of irremediability. Results: The clinician must be aware of the eligibility requirements for granting MAiD in their jurisdiction so that they can appropriately prepare themselves and their patients for the assessment process. The appraisal of the intolerability of the specific person's suffering comes from having an extensive dialogue with the patient; however, the assessment of whether the patient has irremediable BPD should be more objectively and reliably determined. A systematic approach to the assessment of irremediability of BPD is reviewed in the context of the disorder's severity, treatment resistance and irreversibility. Conclusion: In addition to characterizing irremediability, this paper also addresses the evaluation and management of suicide risk for patients with BPD undergoing the MAiD assessment process.
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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 | 0.013 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".