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Record W4313413494 · doi:10.3390/diagnostics13010080

Spectrum of Bacterial Pathogens from Urinary Infections Associated with Struvite and Metabolic Stones

2022· article· en· W4313413494 on OpenAlexaffabout
Adam Haliński, Kamran Hassan Bhatti, Luca Boeri, Jonathan Cloutier, Kaloyan Davidoff, Ayman Elqady, Goran Fryad, Mohamed Gadelmoula, Hongyi Hui, Kremena Petkova, Elenko Popov, Rawa Bapir, Iliya Saltirov, Francisco R. Spivacow, B. M. Zeeshan Hameed, Katarzyna Arkusz, Alberto Trinchieri, Noor Buchholz

Bibliographic record

VenueDiagnostics · 2022
Typearticle
Languageen
FieldMedicine
TopicKidney Stones and Urolithiasis Treatments
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsStruviteUrineUrinary systemMedicineInternal medicineMicrobiologyVeterinary medicineBiologyPhosphate

Abstract

fetched live from OpenAlex

Objective: The purposes of this multi-center study were to evaluate the rate of infection stones and to evaluate the urine cultures of patients with infection stones. Materials: Charts of adulpatients with urinary stones were reviewed and data on stone analyses and urine cultures were collected. Results: In total, 1204 renal stone formers (RSFs) from 10 countries were included (776 males, 428 females). Fifty-six patients (4.6%) had struvite stones. The highest frequency of struvite stones was observed in India (23%) and Pakistan (18%). Lower rates were reported in Canada (2%), China (3%), Argentina (3%), Iraq (3%), Italy (3.5%) and Poland (3%), and intermediate rates in Egypt (5.5%) and Bulgaria (5.4%). Urine cultures were retrieved from 508 patients. Patients with struvite stones had a positive culture in 64.3% of the samples and patients with other stones, in 26.7%. In struvite stones, the most common isolates were Escherichia coli (27.7%) and Proteus spp. (27.7%), followed by Klebsiella spp. (16.7%); in other types of stone, it was Escherichia coli (47.6%), followed by Gram-positive bacteria (14.0%) Conclusions: The struvite stone composition was associated with a urinary infection, although an infection was not demonstrable with a conventional midstream urine culture in about 30%.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.424

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.010
GPT teacher head0.233
Teacher spread0.222 · 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

Citations18
Published2022
Admission routes2
Has abstractyes

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