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Record W4405250087

Subclinical bacteriuria and surgical-site infection in 140 dogs with orthopedic and neurological conditions.

2024· article· en· W4405250087 on OpenAlexaff
Maria Dolores Porcel Sánchez, Dominique D. Gagnon, Brigitte A. Brisson, Katie Hoddinott, Tristan Juette, Mila Freire

Bibliographic record

VenuePubMed · 2024
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Medicine and Surgery
Canadian institutionsUniversity of GuelphUniversity of Prince Edward Island
Fundersnot available
KeywordsSubclinical infectionOrthopedic surgeryMedicineSurgical site infectionBacteriuriaIntensive care medicineSurgeryInternal medicineUrine
DOInot available

Abstract

fetched live from OpenAlex

Objectives: This study aimed to determine the prevalence, risk factors, and types of bacterial isolates associated with subclinical bacteriuria (SBU) in dogs with reduced mobility; and to explore the influence of SBU on surgical-site infection (SSI) in dogs treated surgically for their conditions. Animals: We studied 140 client-owned dogs. Procedure: Medical records of dogs with orthopedic and neurological conditions from 3 academic referral hospitals were reviewed. Dogs receiving antimicrobials or showing lower urinary tract signs were excluded. Using generalized linear models, clinical, procedural, and postoperative variables were evaluated as possible risk factors for SBU and SSI. Results: spp. in 1 dog). Four of the 10 dogs that developed SSI received postoperative antimicrobial therapy. The prevalence of SBU and types of bacterial isolates were similar to those in previous reports. Significant risk factors for developing SBU and its association with SSI were not identified. Conclusion and clinical relevance: Screening and treating for SBU preoperatively remains controversial.

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)0.000
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 score0.044
Threshold uncertainty score0.445

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.061
GPT teacher head0.309
Teacher spread0.248 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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