A Canadian Academic Hospital’s Initial MAID Experience: A Health-Care Systems Review
Bibliographic record
Abstract
BACKGROUND:: Following the Supreme Court of Canada's Carter Decision, medical assistance in dying (MAID) became possible with individual court orders in February 2016. Subsequently, on June 17, 2016, legislation was passed that eliminated the need for court orders, essentially making physicians the arbiters of these requests. Canadian health-care facilities now face the challenge of addressing this unprecedented patient health-care need. AIM:: To describe the manner in which London Health Sciences Center has approached local and regional requests for MAID, including the administration, ethics, privacy, and clinical process. DESIGN:: A health-care systems descriptive study. SETTING/PARTICIPANTS:: Between June 6, 2016, and May 30, 2018, London Health Sciences Center's MAID Internal Resource Committee triaged and referred 260 cases. Ninety-six received the requisite assessments were deemed eligible for and received MAID. RESULTS:: The procedure was completed in hospital 59 (61%) times, and 37 (39%) times in the community (either private residence or long-Term Care facility). Nineteen patients did not meet MAID criteria and 63 patients died while awaiting the procedure. The median wait time between first request and referral was 1 day. The median time between referral and the procedure was 12.0 days. The ratio of referrals to completed cases is 96 of 260 (or 37% conversion rate). CONCLUSION:: Our MAID processes, including our committee structure, referral triage process, and physical site have all undergone extensive review and improvement cycles throughout these first 2 years with the aim of ensuring that this procedure is managed in a respectful, confidential, safe, efficient, and patient-centered manner.
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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 | 0.017 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.034 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".