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Record W1521284793 · doi:10.1111/jorc.12097

AN APPRAISAL OF END‐OF‐LIFE CARE IN PERSONS WITH CHRONIC KIDNEY DISEASE DYING IN HOSPITAL WARDS

2014· article· en· W1521284793 on OpenAlexfundno aff
Helen Noble, Joan Brown, Joanne Shields, Damian Fogarty, Alexander P. Maxwell

Bibliographic record

VenueJournal of Renal Care · 2014
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsnot available
FundersQueen's UniversityDepartment of Social Services, Australian GovernmentQueen's University BelfastScottish Government
KeywordsMedicineKidney diseaseEnd-of-life carePlace of deathDiseaseHealth professionalsHealth careIntensive care medicineEmergency medicinePalliative careNursingInternal medicine

Abstract

fetched live from OpenAlex

AIM: To review end-of-life care provided by renal healthcare professionals to hospital in-patients with chronic kidney disease, and their carers, over a 12-month period in Northern Ireland. METHODS: Retrospective review of 100 patients. RESULTS: Mean age at death was 72 years (19-95) and 56% were male. Eighty three percent of patients had a 'Not For Attempted Resuscitation' order during their last admission and this was implemented in 42%. Less than 20% of all patients died in a hospital ward. No patients had an advanced care plan, although 42% had commenced the Liverpool Care Pathway for the Dying Patient. Patients suffered excessive end-of-life symptoms. In addition, there was limited documentation of carer involvement and carer needs were not formally assessed. CONCLUSION: End-of-life care for patients with advanced chronic renal disease can be enhanced. There is significant variation in the recording of discussions regarding impending death and little preparation. There is poor recording of the patients' wishes regarding death. Those with declining functional status, including those frequently admitted to hospital require holistic assessment regarding end-of-life needs. More effective communication between the patient, family and multi-professional team is required for patients who are dying and those caring for them.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.398

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.005
GPT teacher head0.261
Teacher spread0.256 · 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

Citations8
Published2014
Admission routes1
Has abstractyes

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