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Record W3035224707 · doi:10.1016/j.xkme.2020.05.008

Advanced CKD Care and Decision Making: Which Health Care Professionals Do Patients Rely on for CKD Treatment and Advice?

2020· article· en· W3035224707 on OpenAlexfundno aff
Tyler M. Barrett, Jamie A. Green, Raquel C. Greer, Patti L. Ephraim, Sarah B. Peskoe, Jane Pendergast, Tara S. Strigo, Evan Norfolk, Ion D. Bucaloiu, Clarissa J. Diamantidis, Felicia Hill‐Briggs, Teri Browne, George L. Jackson, L. Ebony Boulware, Clare Il’Giovine, Jon Billet, Jason Browne, Charlotte Collins, Daniel E. Davis, Sherri Fulmer, Jamie Green, Michelle Richner, Cory Siegrist, Wendy Smeal, Rebecca Stametz, Mary Solomon, Christina Yule, Navdeep Tangri, Brian Bankes, Shakur Bolden, Patricia Danielson, Katina Lang‐Lindsey, Suzanne Ruff, Lana Schmidt, Amy Swoboda, Peter Woods, Diana Clynes, Stephanie Stewart, Dori Schatell, Kristi Klicko, Brandi Vinson, Jennifer St. Clair Russell, Kelli K. Collins, Jennifer A. Martin, Dale Singer, Diane Littlewood

Bibliographic record

VenueKidney Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsnot available
FundersAmerican Association of Kidney PatientsUniversity of ManitobaUniversity of South CarolinaJohns Hopkins UniversityPatient-Centered Outcomes Research InstituteNational Kidney Foundation
KeywordsMedicineNephrologyInternal medicineKidney diseaseLogistic regressionDemographicsFamily medicineOdds ratioOddsHealth careCross-sectional studyDialysisIntensive care medicinePathologyDemography

Abstract

fetched live from OpenAlex

Rationale & Objective Chronic kidney disease (CKD) care is often fragmented across multiple health care providers. It is unclear whether patients rely mostly on their nephrologists or non-nephrologist providers for medical care, including CKD treatment and advice. Study Design Cross-sectional study. Setting & Participants Adults receiving nephrology care at CKD clinics in Pennsylvania. Predictors Frequency, duration, and patient-centeredness (range, 1 [least] to 4 [most]) of participants' nephrology care. Outcome Participants' reliance on nephrologists, primary care providers, or other specialists for medical care, including CKD treatment and advice. Analytical Approach Multivariable logistic regression to quantify associations between participants' reliance on their nephrologists (vs other providers) and their demographics, comorbid conditions, kidney function, and nephrology care. Results Among 1,412 patients in clinics targeted for the study, 676 (48%) participated. Among these, 453 (67%) were eligible for this analysis. Mean age was 71 (SD, 12) years, 59% were women, 97% were white, and 65% were retired. Participants were in nephrology care for a median of 3.8 (IQR, 2.0-6.6) years and completed a median of 4 (IQR, 3-5) nephrology appointments in the past 2 years. Half (56%) the participants relied primarily on their nephrologists, while 23% relied on primary care providers, 18% relied on all providers equally, and 3% relied on other specialists. Participants' adjusted odds of relying on their nephrologists were higher for those in nephrology care for longer (OR, 1.08 [95% CI, 1.02-1.15]; P =0.02), those who completed more nephrology visits in the previous 2 years (OR, 1.16 [95% CI, 1.05-1.29]; P =0.005), and those who perceived their last interaction with their nephrologists as more patient-centered (OR, 2.63 [95% CI, 1.70-4.09]; P <0.001). Limitations Single health system study. Conclusions Many nephrology patients relied on non-nephrologist providers for medical care. Longitudinal patient-centered nephrology care may encourage more patients to follow nephrologists' recommendations.

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)0.007
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.003
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.019
GPT teacher head0.348
Teacher spread0.329 · 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 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

Citations27
Published2020
Admission routes1
Has abstractyes

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