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Record W2726535593 · doi:10.1093/geroni/igx004.4946

OUTCOMES OF A QUALITY IMPROVEMENT INTERVENTION TO REDUCE UNNECESSARY URINARY CATHETER UTILIZATION

2017· article· en· W2726535593 on OpenAlexaff
Richard Norman, Rebecca Ramsden, Leanne Ginty, Sharmili Sinha

Bibliographic record

VenueInnovation in Aging · 2017
Typearticle
Languageen
FieldMedicine
TopicUrinary Tract Infections Management
Canadian institutionsSinai Health SystemUniversity of Toronto
Fundersnot available
KeywordsMedicineUrinary systemPsychological interventionUrinary catheterizationCohortIncidence (geometry)Emergency medicineCatheterIntensive care medicineSurgeryInternal medicineNursing

Abstract

fetched live from OpenAlex

Urinary catheters are often placed unnecessarily, exposing patients to complications including urinary tract infections, falls, deconditioning, pressure ulcers and death. As older adults are at particular risk of these sequelae, we introduced a multi-modal intervention incorporating educational posters, small group teaching sessions, and changes to the hospital’s computer physician order entry and nursing documentation systems to encourage evidence-based utilization of urinary catheters. A total of 24,499 patient admissions were included during the overall 41month study period. A quasi-experimental interrupted time series study was designed using segmented regression to assess outcome indicators. Across the services under study, mean catheter days per patient decreased by between 5.6 and 10.0 days (p < 0.01), while the absolute incidence of urinary catheterization almost halved across the same cohort (p < 0.01) following the intervention. The results from this study suggest that a relatively simple bundle of interventions can result in dramatic health outcome improvements.

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)0.005
metaresearch head score (Gemma)0.013
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.096
GPT teacher head0.422
Teacher spread0.325 · 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
Published2017
Admission routes1
Has abstractyes

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