MétaCan
Menu
Back to cohort
Record W3217567382 · doi:10.5770/cgj.24.520

Characteristics of Older Adults Accessing Medical Assistance in Dying (MAiD): a Descriptive Study

2021· article· en· W3217567382 on OpenAlexaffvenueabout
Debbie Selby, Brandon Chan, Amy Nolen

Bibliographic record

VenueCanadian Geriatrics Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsUniversity of TorontoWestern UniversitySunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineDemographicsAutonomyGerontologyDemographyTertiary careMalignancyFamily medicineInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Medical Assistance in Dying (MAiD) is an end-of-life option for Canadians accounting for 2% of all deaths in Canada in 2019. Adults over 80 years old represent a significant proportion of these deaths, yet little is known about how they compare with their younger counterparts. METHODS: This study retrospectively reviewed our tertiary care institution's MAiD database to compare MAiD recipients <65, 65-80, and >80 years of age. Extracted data included basic demographics, illness characteristics, functional status, social living arrangements/contacts, and outcomes of MAiD assessments. RESULTS: Of 267 patients assessed for MAiD, 38.2% were over 80. Compared to the younger groups, those over 80 were more likely to be female, to live alone, and to be widowed; however, they did not self-identify as 'socially isolated'. The majority fit into the illness categories of malignancy, cardiopulmonary or neurologic diseases, but those over 80 were more likely to have other more chronic/subacute conditions leading to the MAiD request. CONCLUSIONS: Older adults accessing MAiD are distinct in that they tend to be increasingly frail and without a predominant underlying diagnosis as compared with younger adults, but rather have an accumulation of losses resulting in global functional decline and subsequent loss of autonomy and independence.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.065
GPT teacher head0.361
Teacher spread0.295 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2021
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Geriatrics JournalSame topicPalliative Care and End-of-Life IssuesFrench-language works237,207