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Record W2556037422 · doi:10.1155/2016/2625870

Process Mapping in a Pediatric Emergency Department to Minimize Missed Urinary Tract Infections

2016· article· en· W2556037422 on OpenAlexaff
Morgan Black, Valene Singh, Vladimir Belostotsky, Madan Roy, Deborah Yamamura, Kathryn Gambarotto, Keith K Lau, April Kam

Bibliographic record

VenueInternational Journal of Pediatrics · 2016
Typearticle
Languageen
FieldMedicine
TopicPediatric Urology and Nephrology Studies
Canadian institutionsHamilton General HospitalMcMaster Children's HospitalUniversity of TorontoMcMaster University
Fundersnot available
KeywordsMedicineEmergency departmentUrinary systemPsychological interventionEmergency medicinePediatricsUrineIntensive care medicineInternal medicineNursing

Abstract

fetched live from OpenAlex

Urinary tract infections (UTIs) are common in young children and are seen in emergency departments (EDs) frequently. Left untreated, UTIs can lead to more severe conditions. Our goal was to undertake a quality improvement (QI) initiative to help minimize the number of children with missed UTIs in a newly established tertiary care pediatric emergency department (PED). A retrospective chart review was undertaken to identify missed UTIs in children < 3 years old who presented to a children's hospital's ED with positive urine cultures. It was found that there was no treatment or follow-up in 12% of positive urine cultures, indicating a missed or possible missed UTI in a significant number of children. Key stakeholders were then gathered and process mapping (PM) was completed, where gaps and barriers were identified and interventions were subsequently implemented. A follow-up chart review was completed to assess the impact of PM in reducing the number of missed UTIs. Following PM and its implementation within the ED, there was no treatment or follow-up in only 1% of cases. Based on our results, the number of potentially missed UTIs in the ED decreased dramatically, indicating that PM can be a successful QI tool in an acute care pediatric setting.

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)0.001
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 score0.014
Threshold uncertainty score0.455

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.026
GPT teacher head0.320
Teacher spread0.294 · 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

Citations7
Published2016
Admission routes1
Has abstractyes

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