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Record W4404022148 · doi:10.1136/bmj.q2423

How has medical assistance in dying changed palliative care?

2024· editorial· en· W4404022148 on OpenAlexaffabout
Leonie Herx

Bibliographic record

VenueBMJ · 2024
Typeeditorial
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPalliative careMedicineData scienceWorld Wide WebNursingComputer science

Abstract

fetched live from OpenAlex

How has medical assistance in dying changed palliative care?Leonie Herx palliative care doctor I've been consulting in palliative medicine for 17 years.I've seen patients facing frightening diagnoses and hospital admission experience a loss of dignity and worry about burdening loved ones.The cornerstone of palliative care is to reaffirm patients' inherent dignity, helping them live well as they navigate the challenges of serious illness, as rooted in the work of Cicely Saunders, UK physician and founder of the modern hospice.In the words of Dame Cicely, reminding patients that "they matter.Canada's medical assistance in dying (MAID) laws have affected my field more quickly than I'd expected.There's now this sense that by wanting to address the patient's suffering palliative care doctors are trying to convince someone out of getting an assisted death.

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)0.014
metaresearch head score (Gemma)0.061
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.024
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.061
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0040.002
Science and technology studies0.0040.004
Scholarly communication0.0100.007
Open science0.0050.002
Research integrity0.0240.027
Insufficient payload (model declined to judge)0.0110.008

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 head0.153
GPT teacher head0.451
Teacher spread0.298 · 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
GenreEditorial

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
Published2024
Admission routes2
Has abstractyes

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