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Record W3081771779 · doi:10.5770/cgj.23.409

Burning for Treatment: Impact of Staff Education on Asymptomatic Bacteriuria Management in the Elderly

2020· article· en· W3081771779 on OpenAlexaffvenue
Casara Hong, Gregory Egan, Byron Sherk

Bibliographic record

VenueCanadian Geriatrics Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicUrinary Tract Infections Management
Canadian institutionsUniversity of British ColumbiaVancouver General HospitalVancouver Coastal Health
Fundersnot available
KeywordsMedicineIncidence (geometry)CohortBacteriuriaUrinary systemUrineAsymptomaticCohort studyAuditEmergency medicineInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Studies indicate that elderly patients are often inappropriately treated with antimicrobials for asymptomatic bacteriuria (ASB). Interprofessional education may help improve the assessment and management of ASB. METHODS: Retrospective chart audits were conducted on two cohorts of positive urine cultures (n = 201) obtained from a geriatric acute care unit to determine the incidence of treated ASB. The first cohort (n = 101) was analyzed from January to July 2017. Education was provided to unit staff (e.g., nurses, physicians, pharmacists) in Fall 2017. The second cohort (n = 100) was analyzed from January to July 2018. Descriptive statistics were used to summarize and compare the results from the cohorts. RESULTS: 152 patients (n = 201 positive urine cultures) were reviewed: 74% (159) of positive urine cultures were ASB and 21% (42) were urinary tract infections. The incidence of treated ASB was 15% (30) and untreated ASB was 65% (129). The incidence of UTI, treated ASB, and untreated ASB were not significantly different between the two cohorts examined. CONCLUSION: The implementation of education did not result in lasting changes in ASB management. Our study suggests that future systemic solutions are necessary to reduce the incidence of treated ASB in the geriatric population.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.850
Threshold uncertainty score0.375

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.024
GPT teacher head0.299
Teacher spread0.275 · 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 designOther design
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

Citations3
Published2020
Admission routes2
Has abstractyes

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