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Record W2774991842 · doi:10.1093/hsw/hlx050

Advance Care Planning for Patients with End-Stage Renal Disease

2017· article· en· W2774991842 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHealth & Social Work · 2017
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsAdvance care planningAutonomyDocumentationMedicineVulnerability (computing)End stage renal diseaseSocial workPsychologyDiseaseNursingIntensive care medicinePalliative careComputer sciencePathologyPolitical science

Abstract

fetched live from OpenAlex

Advance care planning (ACP) may seem straightforward to some. However, for many people this is a challenging topic to discuss and a complex landscape to navigate. This work can be time consuming and emotionally charged, requiring the collaboration of several team members for positive outcomes to result. As a renal social worker in an acute care setting, I see ACP surfacing frequently as a discussion topic. I have seen cases ranging from patients not participating in any ACP to very detailed ACP documentation. This Viewpoint will discuss ACP, explore both the negatives and positives of ACP, and focus in on a study that found a certain model for ACP to be successful. Throughout this text, I will reference the case of Ms. B to help further demonstrate that ACP is a valuable tool for patients with chronic kidney disease, despite some challenges and drawbacks. When working with these patients, the aim is to protect and promote patient autonomy during times of high vulnerability.

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.

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 score0.216
Threshold uncertainty score0.586

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.108
GPT teacher head0.467
Teacher spread0.359 · 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