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Record W2169484437 · doi:10.2215/cjn.05870513

Should an Elderly Patient with Stage V CKD and Dementia Be Started on Dialysis?

2013· article· en· W2169484437 on OpenAlexaff
Irene Ying, Zoe Levitt, Sarbjit V. Jassal

Bibliographic record

VenueClinical Journal of the American Society of Nephrology · 2013
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineDialysisDementiaIntensive care medicineBeneficenceDiseasePopulationEnd stage renal diseasePsychiatryInternal medicineAutonomy

Abstract

fetched live from OpenAlex

The burden of cognitive impairment appears to increase with progressive renal disease, such that the prevalence of dementia among those starting dialysis, or those already established on dialysis, is high. The appropriateness of dialysis initiation in this population has been questioned, and current Renal Physician Association guidelines suggest forgoing dialysis in individuals who have dementia and lack awareness of self and environment. Patients are, however, also entitled to equal rights and respect, equal access to health care services, and an opportunity to engage in shared decision-making processes, particularly if there is concern over reversibility of disease. This article discusses, on the basis of principles of beneficence and nonmaleficence, the arguments in favor of and against dialysis use, and the process of determining an appropriate care plan. Factors discussed include the current societal trend toward a technological imperative, premature fatalism, survival benefits, and the implications of providing care to patients who are unable to express their tolerance for symptoms associated with the treatment or lack of treatment.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.007
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.003
Open science0.0000.001
Research integrity0.0070.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.046
GPT teacher head0.339
Teacher spread0.293 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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

Citations20
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueClinical Journal of the American Society of Nephrology→Same topicDialysis and Renal Disease Management→French-language works237,207→