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Medical assistance in dying (MAiD) in patients with cancer.

2022· article· en· W4286297006 on OpenAlexaffabout
Sara Moore, Chloé Thabet, Paul Wheatley‐Price

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsUniversity of OttawaOttawa Hospital
Fundersnot available
KeywordsMedicineCancerPopulationCancer registryInternal medicineDiseaseLung cancer

Abstract

fetched live from OpenAlex

12028 Background: Medical assistance in dying (MAiD) was legalized in Canada in 2016. Cancer accounts for 60-70% of MAiD cases, though little is known about the demographic profile, cancer diagnoses, and treatments received in patients with cancer who pursue MAiD. We reviewed all patients with cancer who underwent MAiD through a large regional MAiD program, in order to better understand this population and identify gaps in the current system of care delivery. Methods: All patients with cancer who received MAiD through the Champlain Regional MAiD Network (CRMN) from June 1 2016 – November 30 2020 were reviewed. The CRMN provides the majority of MAiD services covering a population of 1.3 million in Eastern Ontario. Baseline demographic factors, diagnostic information, and treatment details were collected by retrospective review. The primary endpoint was the proportion of patients with an oncology consultation prior to MAiD. Results: During the study period, 255 patients with cancer underwent MAiD. Baseline characteristics included: median age at death 71 (range 31-100), 51% male, 56% married/common-law. The most prevalent solid tumors were gastrointestinal [GI] (n = 77, 30%), lung (n = 47, 18%), and genitourinary [GU] (n = 35, 14%). Most patients (n = 201, 79%) had metastatic disease at the time of MAiD. Of those without metastatic disease at time of death, common tumor sites included central nervous system (42%) and head and neck (23%). The majority of patients (n = 229, 89%) had seen an oncology specialist prior to MAiD; 226 (88%) had seen a systemic oncologist (medical, hematologic, or gynecologic oncologist), and 189 (69%) a radiation oncologist. Seventy-three percent of patients were followed by a systemic oncologist within 90 days of MAiD, and 44% within 30 days of MAiD. At least one line of systemic therapy was received by 159 (62%) patients, 138 (54%) received radiotherapy, and 61 (24%) best supportive care alone. Median time from last systemic therapy to MAiD was 85 days, and from last radiation therapy to MAiD was 137 days. Palliative care assessed at least 213 patients (84% [8% unknown]). Common reasons for pursuing MAiD included disease-related symptoms (33%), fear of future suffering or disability (19%), and ability to control the time and manner of death (17%). Among 26 patients who had not seen an oncologist, median age was 84 (range 61-100), 77% male, 42% GI primary / 19% GU / 15% lung. Most had seen a palliative care specialist (n = 23, 88%), and in the remaining 3 patients palliative care involvement was unknown. Conclusions: MAiD is a relatively new option for patients with cancer in Canada. The vast majority of patients with cancer who pursue MAiD are diagnosed with advanced/incurable disease, and most have met with an oncology specialist. As cancer treatments become more effective and more tolerable, collaboration between oncologists and MAiD providers is required to ensure patients are well informed of treatment options prior to MAiD.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.139
Threshold uncertainty score0.277

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.277
GPT teacher head0.565
Teacher spread0.289 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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
Published2022
Admission routes2
Has abstractyes

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