Provision of medical assistance in dying: a scoping review
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study is to map the characteristics of the existing medical literature describing the medications, settings, participants and outcomes of medical assistance in dying (MAID) in order to identify knowledge gaps and areas for future research. DESIGN: Scoping review. SEARCH STRATEGY: We searched electronic databases (MEDLINE, EMBASE, PsychINFO, CINAHL and CENTRAL), clinical trial registries, conference abstracts and professional guidelines from jurisdictions where MAID is legal, up to February 2020. Eligible report types included technical summaries, institutional policies, practice surveys, practice guidelines and clinical studies that describe MAID provision in adults who have provided informed consent for MAID. RESULTS: 163 articles published between 1989 and 2020 met eligibility criteria. 75 studies described details for MAID administered by intravenous medications and 50 studies provided data on oral medications. In intravenous protocols, MAID was most commonly administered using a barbiturate (34/163) or propofol (22/163) followed by a neuromuscular blocker. Oral protocols most often used barbiturates alone (37/163) or in conjunction with an opioid medication (7/163) and often recommended using a prokinetic agent prior to lethal drug ingestion. Complications included prolonged duration of the dying process, difficulty in obtaining intravenous access and difficulty in swallowing oral agents. Most commonly, the role of physicians was prescribing (83/163) and administering medications (75/163). Nurses' roles included administering medications (17/163) and supporting the patient (16/163) or family (13/163). The role of families involved providing support to the patient (17/163) and bringing medications from the pharmacy for self-administration (4/163). CONCLUSIONS: We identified several trends in MAID provision including common medications and doses for oral and parenteral administration, roles of healthcare professionals and families, and complications that may cause patient, family and provider distress. Future research should aim to identify the medications, dosages, and administration techniques and procedures that produce the most predictable outcomes and mitigate distress for those involved.
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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 | 0.022 | 0.086 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.043 | 0.045 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".