Surgical Site Infections in a Small Animal Hospital in a Developing Country
Bibliographic record
Abstract
Surgical Site Infections (SSIs) are the most frequently occurring type of complications associated with small animal surgical procedures and the most common of all healthcare-associated infection in small animal practice. SSI rates are one of the outcome measures for the success of surgical procedures while risk factors are determinants of the burden associated with occurrence of SSIs. It has been demonstrated in human medicine that SSI rates vary significantly between developing and developed countries. Generally, risk factors are the similar between these groups of countries, however, there may be additional factors that account for the added burden of these infections. It is assumed that similar differences exist for small animal surgical practice among developing and developed nations. This study sought to better understand the challenges and constraints associated with the practice of small animal surgery in a developing, tropical country by determining SSI rates, risk factors, antibiotic use practices as well as the effectiveness of cleaning and disinfection practices, using the Small Animal Clinic, Veterinary Teaching Hospital, School of Veterinary Medicine, the University of the West Indies (VTH-UWI), Trinidad and Tobago, as a model. Small animal SSI rate over a three-year period for the VTH-UWI was 8.5%; the SSI rate for dogs was 8.6% while the rates for cats was 6.5%. Risk factors associated with development of SSIs in dogs are being a purebred dog other than a Rottweiler, Belgian Malinois, Labrador Retriever or Doberman Pinscher, increasing body weight, unknown ASA status and more than two surgical procedures per anesthetic period. Regression analysis could not be performed for cats as only a small number (two) were SSI positive. The classes of antibiotics most commonly used in the perioperative period were penicillins and cephalosporins. There were a limited number of culture and sensitivity panels performed, but these showed multidrug resistance showing the penicillins and cephalosporins to be ineffective. The cleaning and disinfection practices of the VTH-UWI were considered substandard. Poor infrastructure design; limited material, equipment and personnel resources as well as absence of a written infection prevention and control program were factors that contributed to deficits in the process. Data from the VTH-UWI show that differences exist between developing and developed countries with respect to the challenges associated with small animal SSIs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".