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Record W4312212508 · doi:10.1016/j.apjon.2022.100181

Evolving strategies to relieve suffering at end-of-life

2022· editorial· en· W4312212508 on OpenAlexafffundabout
Sally Thorne

Bibliographic record

VenueAsia-Pacific Journal of Oncology Nursing · 2022
Typeeditorial
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsPsychologyHistory

Abstract

fetched live from OpenAlex

Oncology nurses and nurses who specialize in palliative care are experts in being attentive to suffering, even when it is expressed in complex ways, and in finding creative medical and supportive care approaches toward relieving it to the extent possible. For patients who are nearing death, pain and symptom management, palliative care, voluntarily stopping eating and drinking, and palliative sedation are alternatives that nurses may have discussed with their patients at various times. For patients with cancer in most of the world, these have traditionally been the only available options for those who experience—or may be anxious about the possibility of experiencing—intolerable suffering in the later stages of a cancer illness and seek to sustain some control over how their dying process unfolds.

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)0.009
metaresearch head score (Gemma)0.012
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: Editorial · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0030.005
Scholarly communication0.0060.007
Open science0.0030.008
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0070.003

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 head0.066
GPT teacher head0.424
Teacher spread0.358 · 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

Citations1
Published2022
Admission routes3
Has abstractyes

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