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Record W2969793293 · doi:10.1177/0825859719869062

Inpatient Palliative Care Consult: A Marker for High Risk of Readmission or Death in Discharged Oncology Inpatients

2019· article· en· W2969793293 on OpenAlexaff
Debbie Selby, Anita Chakraborty, Audrey Kim, Jeff Myers

Bibliographic record

VenueJournal of Palliative Care · 2019
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsUniversity of TorontoHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicinePalliative careEmergency departmentEmergency medicineAdvance care planningPlace of deathDiseaseIntensive care medicineMedical emergencyInternal medicineNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Emergency department visits or readmission to hospital are common particularly among those with advanced illness. Little prospective data exist on early outcomes specifically for patients seen by a palliative care consult service during their acute care admission, who are subsequently discharged home. METHODS: This study followed 62 oncology patients who had had a palliative care consult during their admission to acute care with weekly phone calls postdischarge for 4 weeks. Events recorded included death, readmission, emergency department visits, and admission to a palliative care unit. RESULTS: By the end of the study, 32 (52%) of 62 had had at least 1 event, (readmission, emergency department visit, or death), with the majority of these occurring in the first 2 weeks postdischarge. The overall 4-week death rate was 14 (22.6%) of 62. CONCLUSIONS: These data suggest that the need for a palliative care consult identifies inpatients at very high risk for early deterioration and underlines the critical importance of advance care planning/goals-of-care discussions by the oncology and palliative care teams to ensure patients and families understand their disease process and have the opportunity to direct their care decisions.

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)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.085
Threshold uncertainty score0.917

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.098
GPT teacher head0.431
Teacher spread0.333 · 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.

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

Citations5
Published2019
Admission routes1
Has abstractyes

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