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Record W3176462481 · doi:10.1136/bmjspcare-2021-002889

Palliative home care and emergency department visits in the last 30 and 90 days of life: a retrospective cohort study of patients with cancer

2021· article· en· W3176462481 on OpenAlexafffundabout
Jennifer Mracek, Madalene A. Earp, Aynharan Sinnarajah

Bibliographic record

VenueBMJ Supportive & Palliative Care · 2021
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsAlberta Health ServicesUniversity of Calgary
FundersM.S.I. Foundation
KeywordsMedicinePalliative careEmergency departmentRetrospective cohort studyGeneralist and specialist speciesCohortQuality of life (healthcare)Odds ratioCancerEnd-of-life careCohort studyOddsLogistic regressionEmergency medicineInternal medicineNursing

Abstract

fetched live from OpenAlex

OBJECTIVES: Evaluate the association of specialist palliative home care (HC) on emergency department (ED) visits in the 30 and 90 days prior to death. METHODS: This retrospective cohort study using administrative data identified 6976 adults deceased from cancer between 2008 and 2015, living ≥180 days after diagnosis of cancer, and residing in the urban Calgary Zone of Alberta Health Services. All palliative HC and generalist HC services were examined. Regression analyses examined the relationships of HC type to ED visits in the last 30 or 90 days of life. RESULTS: 1.54; 95% CI 1.31 to 1.82). In the last 90 days of life, compared with patients receiving palliative HC, those receiving generalist HC (OR 1.48; 95% CI 1.32 to 1.67) and no HC (OR 1.66; 95% CI 1.39 to 1.99) had increased odds of visiting the ED. CONCLUSIONS: Receiving generalist HC and no HC was associated with increased odds of visiting the ED in the last 30 and 90 days of life, when compared with patients receiving palliative HC. Improving access to palliative HC for patients at high risk of visiting the ED may reduce ED visits and acute care costs and improve quality of life in the last 90 days of life.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.044
GPT teacher head0.383
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 teacher head, not a consensus.

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

Citations8
Published2021
Admission routes3
Has abstractyes

Explore more

Same venueBMJ Supportive & Palliative CareSame topicPalliative Care and End-of-Life IssuesFrench-language works237,207