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Record W3109135370 · doi:10.9734/jpri/2020/v32i2830878

Incidence of Urinary Tract Infection among Diabetic Patients in Abakaliki Metropolis

2020· article· en· W3109135370 on OpenAlexaff
Stella Chinenye Kama, Emmanuel Ifeanyi Obeagu, Moses Nnaemeka Alo, Kingsley Ochei, Uchenna Modestus Ezugwu, Michael Odo, Mabel Ikpeme, Chukwulete Okafor Ukeekwe, Augustine Amaeze Amaeze

Bibliographic record

VenueJournal of Pharmaceutical Research International · 2020
Typearticle
Languageen
FieldMedicine
TopicUrinary Tract Infections Management
Canadian institutionsHealth Care Foundation
Fundersnot available
KeywordsKlebsiella pneumoniaEnterococcus faecalisUrineProteusMedicineIncidence (geometry)MicrobiologyStreptococcusUrinary systemEnterococcusStaphylococcus aureusPseudomonas aeruginosaKlebsiellaEscherichia coliPneumoniaInternal medicineBacteriaProteus mirabilisBiologyAntibiotics

Abstract

fetched live from OpenAlex

The investigation of urinary tract infection (UTI) among diabetic patients 15-51 years and above was assessed using 100 mid-stream urine specimen with the objective of isolating and identifying different types of bacteria and their respective frequencies among diabetic patients attending diabetic clinic at Alex Ekwueme Federal University Teaching Hospital, Abakaliki. A urine culture was performed combined with a full report of urine to establish the diagnosis. The result showed that the majority of bacteria in urinary tract infections were in 27-32 years of age group (71.4%) and lowest in 15-20 years age group (0%). The predominant bacteria isolates and their percentage occurrences include; Escherichia coli (39.13%), Klebsiella pneumonia (21.74%), Proteus (8.69%), Pseudomonas aeruginosa (8.69%), Streptococcus (8.69%), Staphylococcus aureus (6.52%), Enterococcus faecalis (4.25%). There was a high prevalence of the isolated organisms in female (47.7%) compared to males (36%). It follows that most predominant agent of UTI in diabetic patients in Abakaliki Metropolis is Escherichia coli followed by Klebsiella pneumonia.

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)0.003
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 score0.012
Threshold uncertainty score0.975

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
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 integrity0.0000.001
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 head0.135
GPT teacher head0.477
Teacher spread0.342 · 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

Citations6
Published2020
Admission routes1
Has abstractyes

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