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Record W2588639269 · doi:10.1097/mnh.0000000000000321

Hard choices, better outcomes

2017· review· en· W2588639269 on OpenAlexafffund
Janet L. Davis, Sara N. Davison

Bibliographic record

VenueCurrent Opinion in Nephrology & Hypertension · 2017
Typereview
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsUniversity of AlbertaUniversity of Ottawa
FundersCanadian Institutes of Health Research
KeywordsDialysisNephrologyDecision aidsComputer scienceMedicinePopulationClinical decision makingKidney diseaseEnd stage renal diseaseIntensive care medicineInternal medicineDiseaseAlternative medicinePathology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Patients reaching end-stage kidney disease face difficult decisions, including choosing among renal replacement therapies (RRTs). With an increasingly elderly and frail population, there is growing interest in conservative kidney management (CKM) as a viable alternative to dialysis. Shared decision-making (SDM) is a patient-centered approach to these decisions, in which choices are viewed within the explicitly discussed values and preferences of the patient. Patient decision aids (PDAs) are tools designed to facilitate these discussions. The choice between dialysis and CKM is particularly complex, given the poor prognostication data for CKM. This is an emerging area for PDAs in nephrology. This review highlights care gaps around SDM for dialysis versus CKM, presents current PDAs for making choices about RRTs and CKM and discusses exciting new work around the development of novel PDAs. RECENT FINDINGS: Many PDAs have been created recently, primarily to help with decisions about RRTs. Three new PDAs are in testing phases to aid with the more complex decision of choosing between dialysis and CKM. SUMMARY: International nephrology communities are moving toward improved SDM with their patients and PDAs are being developed to facilitate this process.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.936
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.222
GPT teacher head0.421
Teacher spread0.198 · 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.

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

Citations75
Published2017
Admission routes2
Has abstractyes

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