P.117 The opinion of Canadian spine surgeons on medical assistance in dying (MAID); a cross-sectional survey of Canadian spine society (CSS) members
Bibliographic record
Abstract
Background: On February 6, 2015, the Supreme Court of Canada struck down the Criminal Code absolute prohibition on assisted dying, and in June 2016 the new law, Bill C-14, came into effect allowing for medical assistance in dying. We sought to determine the attitudes and opinions of Canadian neurosurgeons and orthopedic spine surgeons regarding MAID. Methods: A cross-sectional survey was sent out to members of the Canadian Spine Society (CSS), which included 21 questions pertaining to opinions regarding MAID. Responses were collected between May-June 2016. Results: A total of 51 surgeons responded to the survey, comprised of a mix of spine surgeons from across the country. The majority of surgeons supported MAID (62.8%), and right of physicians to participate (82.4%). Most surgeons supported the right to conscientious objection (90.1%), but also mandatory duty to refer (49.0%). The conditions most frequently felt to be appropriate for MAID included metastatic spine tumour (76.5%), malignant intramedullary tumour (64.7%), primary malignant spine tumour (54.9%), cervical spinal cord injury with tetraplegia (49.0%) and multiple myeloma (33.3%). Conclusions: This study highlights the complex landscape that exists when discussing MAID, but also the overall support of physicians, and need for ongoing conversations, particularly with issues not addressed by the current legislation.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 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) | 0.005 | 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".