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Record W2064722072 · doi:10.1177/0269216311407694

End-of-life care in hospitalized adults with complex congenital heart disease: care delayed, care denied

2011· article· en· W2064722072 on OpenAlexaff
Daniel Tobler, Matthias Greutmann, Jack M. Colman, Mehtap Greutmann-Yantiri, Lawrence Librach, Adrienne H. Kovacs

Bibliographic record

VenuePalliative Medicine · 2011
Typearticle
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsMount Sinai HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicinePalliative careLife expectancyEnd-of-life careHeart diseaseDiseaseQuality of life (healthcare)Intensive care medicinePediatricsRetrospective cohort studyPopulationInternal medicineNursing

Abstract

fetched live from OpenAlex

Adults with congenital heart disease represent a growing patient population. Notwithstanding dramatic improvements in survival and life expectancy over recent decades, many of these patients remain at risk of premature death from progressive heart disease and would benefit from the principles of palliative and end-of-life care. Data on end-of-life care in this patient group are, however, lacking. We report a retrospective study of 48 patients with congenital heart disease who died while admitted to our hospital (mean age at death 37 ± 14 years). We describe circumstances of death, end-of-life discussions, and the provision of end-of-life care. The majority of patients had complex congenital heart disease and were considered to be in the end stage of their disease. Despite this, only a minority of patients had documented end-of-life discussions prior to their terminal admission and most received continuing aggressive medical treatment up to their demise. Advanced palliative and end-of-life care strategies should be developed for and provided to this group of patients, with the dual aims of reduction of unwarranted therapies and enhancement of the quality of death and dying.

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 score0.055
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.038
GPT teacher head0.310
Teacher spread0.272 · 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

Citations85
Published2011
Admission routes1
Has abstractyes

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