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Record W2898328822 · doi:10.1093/annonc/mdy295.018

Integration of oncology and palliative care

2018· article· en· W2898328822 on OpenAlexaff
S. Kaasa, J.H. Loge, Tit Albreht, Éduardo Bruera, Augusto Caraceni, Andrés Cervantes, Irene J Higginson, Kim Jordan, Per Sjøgren, Patrick Stone, C. Zimmermann, Tonje Lundeby

Bibliographic record

VenueAnnals of Oncology · 2018
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicinePalliative careMultidisciplinary approachCompetence (human resources)OncologyHealth careCommissionFocus groupNursingFamily medicineInternal medicinePsychology

Abstract

fetched live from OpenAlex

Background: Integration of oncology and palliative care (PC) combines two paradigms: the tumour-directed approach, which is the main focus of oncology, and the host-directed approach, which is the focus of PC. Contemporary PC aims to prevent, treat and reduce symptoms and suffering and to preserve and improve quality of life. A Lancet Oncology Commission was written on how PC and oncology can be integrated by combining these two approaches focusing content, models, organization of cancer care, public health, politics, education and research.

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)0.024
metaresearch head score (Gemma)0.040
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: Empirical · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.128

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.040
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0020.020
Scholarly communication0.0080.010
Open science0.0020.016
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0050.001

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 head0.382
GPT teacher head0.553
Teacher spread0.171 · 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
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

Citations6
Published2018
Admission routes1
Has abstractyes

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