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Record W2938132505 · doi:10.1159/000496306

Can Remote Patient Management Improve Outcomes in Peritoneal Dialysis?

2019· review· en· W2938132505 on OpenAlexaff
Longkai Li, Jeffrey Perl

Bibliographic record

VenueContributions to nephrology · 2019
Typereview
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsMedicinePeritoneal dialysisObservational studyIntensive care medicinePsychological interventionTelemedicineSurgeryInternal medicineNursingHealth care

Abstract

fetched live from OpenAlex

Remote patient management (RPM) for patients receiving peritoneal dialysis (PD) is an emerging technology whereby a patients' biometric information (including weight, blood pressure, and other parameters) can be remotely monitored by providers along with important PD treatment information. As part of an evolving field of telemedicine, RPM has the potential to improve clinical outcomes for patients receiving PD, reduce resource utilization, and improve PD uptake, and positively impact a patients' experience with PD. Preliminary reports of RPM are encouraging, however, research in the field is limited to small, single center studies, and remains largely observational; interventions have been heterogenous in terms of what clinical and treatment parameters are being remotely monitored and what outcomes are under evaluation. In the present review, we will describe the evolving field of RPM in PD, discuss potential benefits of the technology while highlighting existing and important future areas for research.

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 score0.987
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
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.017
GPT teacher head0.330
Teacher spread0.313 · 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

Citations8
Published2019
Admission routes1
Has abstractyes

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