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Record W3137507048 · doi:10.2215/cjn.15020920

Digital Applications Targeting Medication Safety in Ambulatory High-Risk CKD Patients

2021· article· en· W3137507048 on OpenAlexafffund
Stephanie W. Ong, Sarbjit V. Jassal, Eveline C. Porter, Kyoyoon K. Min, Akib Uddin, Joseph A Cafazzo, Valeria E. Rac, George Tomlinson, Alexander G. Logan

Bibliographic record

VenueClinical Journal of the American Society of Nephrology · 2021
Typearticle
Languageen
FieldMedicine
TopicMedication Adherence and Compliance
Canadian institutionsLunenfeld-Tanenbaum Research InstituteCentre for Advancing Health OutcomesTed Rogers Centre for Heart ResearchSinai Health SystemCanadian Institutes of Health ResearchDiabetes CanadaToronto Public HealthToronto General HospitalUniversity of TorontoUniversity Health Network
FundersCanadian Institutes of Health Research
KeywordsMedicineInterquartile rangeConfidence intervalAmbulatoryRandomized controlled trialEmergency medicineAdverse effectPolypharmacyInternal medicine

Abstract

fetched live from OpenAlex

Background and objectives Patients with CKD are at risk for adverse drug reactions, but effective community-based preventive programs remain elusive. In this study, we compared the effectiveness of two digital applications designed to improve outpatient medication safety. Design, setting, participants, & measurements In a 1-year randomized controlled trial, 182 outpatients with advanced CKD were randomly assigned to receive a smartphone preloaded with either eKidneyCare ( n =89) or MyMedRec ( n =93). The experimental intervention, eKidneyCare, includes a medication feature that prompted patients to review medications monthly and report changes, additions, or medication problems to clinicians for reconciliation and early intervention. The active comparator was MyMedRec, a commercially available, standalone application for storing medication and other health information that can be shared with patients' providers. The primary outcome was the rate of medication discrepancy, defined as differences between the patient’s reported history and the clinic’s medication record, at exit. Results At exit, the eKidneyCare group had fewer total medication discrepancies compared with MyMedRec (median, 0.45; interquartile range, 0.33–0.63 versus 0.67; interquartile range, 0.40–1.00; P =0.001), and the change from baseline was 0.13±0.27 in eKidneyCare and 0.30±0.41 in MyMedRec ( P =0.007). eKidneyCare use also reduced the severity of clinically relevant medication discrepancies in all categories, including those with the potential to cause serious harm (estimated rate ratio, 0.40; 95% confidence interval, 0.27 to 0.63). Usage data revealed that 72% of patients randomized to eKidneyCare completed one or more medication reviews per month, whereas only 30% of patients in the MyMedRec group (adjusted for dropouts) kept their medication profile on their phone. Conclusions In patients who are high risk and have CKD, eKidneyCare significantly reduced the rate and severity of medication discrepancies, the proximal cause of medication errors, compared with the active comparator. Clinical Trial registry name and registration number: www.ClinicalTrials.gov, NCT02905474.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.099
Threshold uncertainty score0.370

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.025
GPT teacher head0.340
Teacher spread0.315 · 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.

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

Citations26
Published2021
Admission routes2
Has abstractyes

Explore more

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