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Record W4243068769 · doi:10.1017/cjn.2019.210

P.117 The opinion of Canadian spine surgeons on medical assistance in dying (MAID); a cross-sectional survey of Canadian spine society (CSS) members

2019· article· en· W4243068769 on OpenAlexvenueaboutno aff
E Leck, S Barry, S Christie

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2019
Typearticle
Languageen
FieldEngineering
TopicMedical Imaging and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLegislationCervical spineCross-sectional studyGeneral surgeryOrthopedic surgeryTetraplegiaFamily medicineSurgerySpinal cordLawSpinal cord injuryPsychiatryPathology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.204
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.005
Science and technology studies0.0010.005
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.045
GPT teacher head0.283
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes2
Has abstractyes

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