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Record W2595624052 · doi:10.5489/cuaj.4097

Shock due to urosepsis: A multicentre study

2017· article· en· W2595624052 on OpenAlexvenueno aff
Fukashi Yamamichi, Katsumi Shigemura, Koichi Kitagawa, Kei Takaba, Issei Tokimatsu, Soichi Arakawa, Masato Fujisawa

Bibliographic record

VenueCanadian Urological Association Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicUrinary Tract Infections Management
Canadian institutionsnot available
Fundersnot available
KeywordsShock (circulatory)Intensive care medicineMedicinePsychologyInternal medicine

Abstract

fetched live from OpenAlex

Introduction: Urosepsis is a severe infection that can cause shock afterwards. The purpose of this study is to investigate the clinical and bacterial risk factors for shock in those cases with urosepsis caused by urinary tract infection in a multicentre study.Methods: Our study included 77 consecutive urosepsis cases from four hospitals. We examined factors such as patient characteristics, underlying disease, serum white blood cell (WBC) count, platelet count, C-reactive protein (CRP) level at the time of diagnosis of urosepsis, urinary tract occlusion, causative bacteria, and bacterial antibiotic susceptibilities. Statistical analyses were performed to assess the potential risk factors for shock during the clinical course of urosepsis by a multivariate analysis.Results: We had 38 male and 39 female patients aged 25‒104 (median 73). Underlying diseases included cancers (n=22, 28.6 %) and diabetes mellitus (n=17, 22.1 %). Positive blood culture was seen in 74 cases; these involved 88 bacterial strains, of which Escherichia coli was the most common (34 strains, 38.6 %). There were 31 cases with shock (40.3 %) and multivariate analyses demonstrated that serum CRP was the only clinical risk factor for shock due to urosepsis.Conclusions: Our study demonstrated that serum CRP was a risk factor for shock during urosepsis in a multicentre analysis. Further prospective studies with a greater number of patients are needed to draw more definitive conclusions.

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)0.002
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 score0.093
Threshold uncertainty score0.857

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.022
GPT teacher head0.281
Teacher spread0.259 · 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

Citations22
Published2017
Admission routes1
Has abstractyes

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