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Record W7001199357

Investigation into various risk factors associated with surgical site infection in large animals.

2019· article· en· W7001199357 on OpenAlexaboutno aff

Bibliographic record

VenueIslandScholar (University of Prince Edward Island) · 2019
Typearticle
Languageen
FieldMedicine
TopicSurgical site infection prevention
Canadian institutionsnot available
Fundersnot available
KeywordsIncidence (geometry)PerforationReferralEpidemiologyInfection controlOutbreakSurgical site infectionOdds ratioTransmission (telecommunications)
DOInot available

Abstract

fetched live from OpenAlex

This research comprised four studies aiming to contribute to our knowledge regarding factors associated with an increased risk of developing surgical site infection (SSI) in the large animal clinical setting. \n The first study of this thesis reported the prevalence of MRSA in hospitalized horses in a referral hospital in Atlantic Canada and describes the use of surveillance and whole genome sequencing to follow an outbreak of MRSA in this veterinary teaching hospital. This was the first report of MRSA ST398 t011 in horses in North America and intercontinental spread from Europe to North America via veterinary personnel was likely the cause of the reported MRSA outbreak. This highlights that aside from animal movement, movement of veterinary personnel can pose a risk for transmission of MRSA to horses from one geographical region to another. \n The second study determined the incidence of glove perforation in large animal surgery based on water leak test (WLT) and electroconductivity testing (ECT) and identified risk factors for glove perforation. Glove perforation rates in large animal surgery appear to be comparable to those in human and in small animal studies, and ECT was more sensitive than WLT in detecting glove perforations. Factors that increase the odds of glove perforation included the duration a glove was worn, invasiveness of the procedure, and role of the wearer in the surgical procedure. \n The third study compared the ability of four surgical hand preparation techniques to reduce total aerobic bacterial counts on equine surgeon’s hands. While all four hand preparation techniques evaluated in this study were effective based on the Food and Drug Administration’s (FDA’s) Tentative Final Monograph (ASTM E-1115), use of a sole alcohol based agent was less effective than chlorhexidine (CHx) containing products providing evidence that use of CHx still has value for preparation of equine surgeon’s hands. However, the use of a brush scrub technique with CHx showed no benefit compared to rub techniques in reducing bacterial counts. \n The last study’s objective was to describe the change of microbiota on human hands during the process of hand antisepsis and surgery, and similarly, the change in microbiota of the horses’ skin over the course of surgical antisepsis and surgery. Preliminary results of this study did not identify significant changes in microbiota composition from before to after antiseptic preparation of both human and equine skin samples. Further sample processing and analysis needs to be carried out before conclusions from this study can be drawn but quantification of bacterial numbers and assessment of viability remain major challenges with next generation sequencing approaches and may warrant combined approaches of culture-dependent and independent methodologies for a more complete assessment of the effect of antiseptic methods and their efficacy. \n These studies demonstrated that investigation of factors that influence SSI in large animal surgery require a comprehensive approach and are partly limited by low surgical case load and the multifactorial nature of the etiology of SSI. This work did fill gaps in knowledge about factors that had been previously described in other species to influence the rate of SSI.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.220
Teacher spread0.210 · 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 source (direct Gemma or distilled Codex), 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
Published2019
Admission routes1
Has abstractyes

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