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Record W2930451187 · doi:10.1053/j.ajkd.2019.01.030

Use of a Decision Aid for Patients Considering Peritoneal Dialysis and In-Center Hemodialysis: A Randomized Controlled Trial

2019· article· en· W2930451187 on OpenAlexfundno aff
Lalita Subramanian, Junhui Zhao, Jarcy Zee, Megan Knaus, Angela Fagerlin, Erica Perry, June Swartz, Margie McCall, Nicole Bryant, Francesca Tentori

Bibliographic record

VenueAmerican Journal of Kidney Diseases · 2019
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsnot available
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institutes of HealthCanadian Institutes of Health ResearchNational Institute for Health and Care ResearchKing Chulalongkorn Memorial HospitalPatient-Centered Outcomes Research InstituteChulalongkorn UniversityASCRS Research FoundationAstraZenecaRocheAmgen
KeywordsMedicineRandomized controlled trialHemodialysisPeritoneal dialysisDialysisDecision aidsRandomizationKidney diseaseGeneralizability theoryPhysical therapyIntensive care medicineInternal medicineAlternative medicine

Abstract

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Rationale & ObjectiveAnnually, about 100,000 US patients face the difficult choice between the most common dialysis types, in-center hemodialysis and peritoneal dialysis. This study evaluated the value of a new decision aid to assist in the choice of dialysis modality.Study DesignA parallel-group randomized controlled trial to test the efficacy of the decision aid on decision-making outcomes.Setting & ParticipantsEnglish-speaking US adults with advanced chronic kidney disease and internet access enrolled in 2015.InterventionParticipants randomly assigned to the decision aid intervention received information about chronic kidney disease, peritoneal dialysis, and hemodialysis and a value clarification exercise through the study website using their own electronic devices. Participants in the control arm were only required to complete the control questionnaire. Questionnaire responses were used to assess differences across arms in decision-making outcomes.OutcomesTreatment preference, decisional conflict, decision self-efficacy, knowledge, and preparation for decision making.ResultsOf 234 consented participants, 94 (40.2%) were lost to follow-up before starting the study. Among the 140 (70 in each arm) who started the study, 7 were subsequently lost to follow-up. Decision aid users had lower decisional conflict scores (42.5 vs 29.1; P < 0.001) and higher average knowledge scores (90.3 vs 76.5; P < 0.001). Both arms had high decisional self-efficacy scores independent of decision aid use. Uncertainty about choice of dialysis treatment declined from 46% to 16% after using the decision aid. Almost all (>90%) users of the decision aid reported that it helped in decision making.LimitationsLimited generalizability from the study of self-selected study participants who had to have internet access, speak English, and have computer literacy. High postrandomization loss to follow-up. Evaluation of only short-term outcomes.ConclusionsThe decision aid improves decision-making outcomes immediately after use. Implementation of the decision aid in clinical practice may allow further assessment of its effects on patient engagement and empowerment in choosing a dialysis modality.FundingThis study was funded through a Patient Centered Outcomes Research Institute (PCORI) award (#1109).Trial RegistrationRegistered at ClinicalTrials.gov with study number NCT02488317. Annually, about 100,000 US patients face the difficult choice between the most common dialysis types, in-center hemodialysis and peritoneal dialysis. This study evaluated the value of a new decision aid to assist in the choice of dialysis modality. A parallel-group randomized controlled trial to test the efficacy of the decision aid on decision-making outcomes. English-speaking US adults with advanced chronic kidney disease and internet access enrolled in 2015. Participants randomly assigned to the decision aid intervention received information about chronic kidney disease, peritoneal dialysis, and hemodialysis and a value clarification exercise through the study website using their own electronic devices. Participants in the control arm were only required to complete the control questionnaire. Questionnaire responses were used to assess differences across arms in decision-making outcomes. Treatment preference, decisional conflict, decision self-efficacy, knowledge, and preparation for decision making. Of 234 consented participants, 94 (40.2%) were lost to follow-up before starting the study. Among the 140 (70 in each arm) who started the study, 7 were subsequently lost to follow-up. Decision aid users had lower decisional conflict scores (42.5 vs 29.1; P < 0.001) and higher average knowledge scores (90.3 vs 76.5; P < 0.001). Both arms had high decisional self-efficacy scores independent of decision aid use. Uncertainty about choice of dialysis treatment declined from 46% to 16% after using the decision aid. Almost all (>90%) users of the decision aid reported that it helped in decision making. Limited generalizability from the study of self-selected study participants who had to have internet access, speak English, and have computer literacy. High postrandomization loss to follow-up. Evaluation of only short-term outcomes. The decision aid improves decision-making outcomes immediately after use. Implementation of the decision aid in clinical practice may allow further assessment of its effects on patient engagement and empowerment in choosing a dialysis modality.

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)0.030
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.303
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.056
GPT teacher head0.366
Teacher spread0.309 · 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 designRandomized trial
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

Citations53
Published2019
Admission routes1
Has abstractyes

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