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Record W4375955143 · doi:10.1111/hdi.13093

Pathways to improve nephrologist comfort in managing patients on in‐center or home self‐care dialysis

2023· review· en· W4375955143 on OpenAlexvenueno aff
Minghui Huang, Anita Vincent Johnson, Negiin Pourafshar, Rakesh Malhotra, Jason Yang, Monarch Shah, Rasheed A. Balogun, Tushar Chopra

Bibliographic record

VenueHemodialysis International · 2023
Typereview
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsnot available
FundersNational Institute of Diabetes and Digestive and Kidney Diseases
KeywordsHome hemodialysisMedicineDialysisHemodialysisIntensive care medicineNephrologyEmpowermentPatient EmpowermentPatient educationNursingInternal medicine

Abstract

fetched live from OpenAlex

In end-stage kidney disease (ESKD), patient engagement and empowerment are associated with improved survival and complications. However, patients lack education and confidence to participate in self-care. The development of in center self-care hemodialysis can enable motivated patients to allocate autonomy, increase satisfaction and engagement, reduce human resource intensiveness, and cultivate a curiosity about home dialysis. In this review, we emphasize the role of education to overcome barriers to home dialysis, strategies of improving home dialysis utilization in the COVID 19 era, the significance of in-center self-care dialysis (e.g., cost containment and empowering patients), and implementation of an in-center self-care dialysis as a bridge to home hemodialysis (HHD).

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)0.002
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: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.039
GPT teacher head0.323
Teacher spread0.284 · 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
GenreReview

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

Citations6
Published2023
Admission routes1
Has abstractyes

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