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Record W2753178964 · doi:10.3747/co.24.3704

Acute Care Hospitalization Near the End of Life for Cancer Patients Who Die in Hospital in Canada

2017· article· en· W2753178964 on OpenAlexaffvenueabout
K. DeCaria, Deborah Dudgeon, E. Green, Raquel Shaw Moxam, Rami Rahal, Jin Niu, Heather Bryant

Bibliographic record

VenueCurrent Oncology · 2017
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsUniversity of CalgaryQueen's UniversityCanadian Partnership Against Cancer
Fundersnot available
KeywordsMedicineEnd-of-life careAcute hospitalAcute careIntensive care unitCancerHealth careEmergency medicinePalliative careIntensive care medicineNursingInternal medicine

Abstract

fetched live from OpenAlex

Acute care hospitals have a role in managing the health care needs of people affected by cancer when they are at the end of life. However, there is a need to provide end-of-life care in other settings, including at home or in hospice, when such settings are more appropriate. Using data from 9 provinces, we examined indicators that describe the current landscape of acute care hospital use at the end of life for patients who died of cancer in hospital in Canada. Interprovincial variation was observed in acute care hospital deaths, length of stay in hospital, readmission to hospital, and intensive care unit use at the end of life. High rates of acute care hospital use near the end of life might suggest that community and home-based end-of-life care might not be suiting patient needs.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.045
Threshold uncertainty score0.326

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.006
Science and technology studies0.0040.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.114
GPT teacher head0.454
Teacher spread0.340 · 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 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

Citations17
Published2017
Admission routes3
Has abstractyes

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