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Record W1623907942 · doi:10.1111/sdi.12011

Intensive Hemodialysis in the (Nursing) Home: the Bright Side of Geriatric ESRD Care?

2012· article· en· W1623907942 on OpenAlexaff
T. Cornelis, Peter Kotanko, Éric Goffin, Frank M. van der Sande, Jeroen P. Kooman, Christopher T. Chan

Bibliographic record

VenueSeminars in Dialysis · 2012
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsToronto General Hospital
Fundersnot available
KeywordsMedicineIntensive care medicineDialysisHemodialysisIntensive careQuality of life (healthcare)MalnutritionObservational studyPopulationNursing careRehabilitationNursingPhysical therapyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Elderly ESRD patients often lose functionality when they start dialysis, which may be due to a variety of clinical problems. We recently postulated that intensive (longer and/or more frequent) hemodialysis (HD) may be the ideal strategy to try to prevent these ESRD- and dialysis-related complications, including dialysis-induced hypotension, cardiac and cerebral events, malnutrition, infections, sleep problems, and psychological issues. The feasibility of home dialysis therapies has been demonstrated in observational studies. As self-care dialysis is often a challenge in the elderly patient, assisted intensive home HD may facilitate the long-term continuation of this modality. Intensive nursing home HD seems to be an attractive goal for the future because many elderly ESRD patients reside in an extended care facility. Combination with rehabilitation and support by social worker and psychologist remains crucial in the holistic approach toward the elderly ESRD patient. Further studies are required to test the potential protective effects of intensive HD on functionality and quality of life in elderly ESRD patients, and to elucidate the mechanisms underlying frailty and other geriatric syndromes in this highly vulnerable patient population.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.546
Threshold uncertainty score0.511

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
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 head0.009
GPT teacher head0.266
Teacher spread0.257 · 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

Citations9
Published2012
Admission routes1
Has abstractyes

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