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Record W4397048570 · doi:10.1681/asn.20233411s1896c

Medication Reconciliation in Veterans Receiving Dialysis at Edward Hines VA Hospital: A Quality Improvement Initiative

2023· article· en· W4397048570 on OpenAlexaboutno aff
Juan J. Cintrón-García, Roberto Peña Guerrero, Holly Kramer, Karen A. Griffin, Kavitha Vellanki

Bibliographic record

VenueJournal of the American Society of Nephrology · 2023
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDialysisVeterans AffairsQuality managementIntensive care medicineGerontologyEmergency medicineInternal medicineOperations managementEngineering

Abstract

fetched live from OpenAlex

Background: Polypharmacy, commonly defined as use of 5 or more medications, is associated with higher morbidity, fall risk, functional decline and disability. We aimed to identify prevalence of medication discrepancy and possible risk factors in veterans receiving dialysis at Hines VA hospital. Methods: Eligible patients were asked to bring all pill bottles at least once during the study period, October 2022 to March 2023. Medical records were then reviewed to identify discrepancies. Collected data included age, race, sex, cause of end stage renal disease (ESRD), dialysis vintage time, dialysis modality, no: of providers involved, no: of prescribed medications and presence of care giver at home. Cognitive screening was done using Montreal Cognitive Assessment Test (MoCA). Results: A total 48 patients participated in the study. Baseline characteristics are shown in Table 1. 32 patients had at least one medication discrepancy. Compared to patients that completed MoCA (36/48), those that declined (12/48) had higher percentage of medication discrepancies (75% vs. 63%) despite having a caregiver (71% vs. 61%) (Image 1). 11 of the 12 patients declining MoCA were receiving hemodialysis (HD). Conclusions: We found high prevalence of medication discrepancy in our veteran ESRD patients. MoCA scores were not associated with medication discrepancies, but those declining MoCA or with poor overall interest tended to have more discrepancies. Our study limitation is small sample size of a single center veteran population. Funding: Veterans Affairs Support - Baseline characteristics of study population Age in years Average age: 68 Race 29-African American 17-White 2-Other race Sex 46-Male 2-Female Cause of ESRD 29-DM 6-HTN 3-GN 10-Other Dialysis vintage time in years Average 3.7 years Modality of dialysis (HD vs. PD) 41-HD 7-PD No: of providers involved in care Average: 3 No: of prescribed medications Average: 9 Mean MoCA score (N = 36) 20 (total score = 30) Image 1

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)0.014
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 score0.012
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.099
GPT teacher head0.409
Teacher spread0.310 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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