Evaluation of Disinfection Quality of Dental Faculty Units of Ahvaz Jundishapur University of Medical Sciences, Southwest of Iran in 2017
Bibliographic record
Abstract
Introduction: Performing dental practices in the oral environment leads to the transmission of microorganisms in saliva and blood to working surfaces and dental devices and their infection. Preventing transmission of infection through these devices is an important task of a dentist. Hence, this research was conducted to evaluate the disinfection quality of the dental faculty units of Ahvaz Jundishapur University of Medical Sciences (AJUMS) in Iran.Materials and Methods: In order to evaluate the quality of disinfection of the units, sampling was performed from all glasses spittoon surfaces of 90 units of the clinical unit of the AJUMS dental faculty before and after disinfection by personnel. Then, the bacteria were cultured in a medium and examined.Results: The mean (and standard deviation) of the total infection of units of the dental faculty was 46534.4 (583380.4) colonies per 1 ml before disinfection and 40265.6 (52131.1) colonies per 1 ml after disinfection, reflecting significant decrease in number of bacterial colonies after disinfection (P <0.001). In addition, a significant decrease was seen in the number of bacterial colonies in the restoration, pediatric, orthodontic and diagnosis units (p <0.05), but the difference before and after disinfection was not significant in the prosthetic, endodontic, surgical and periodontal units. In addition, the most common types of microorganisms in the whole units of the dental faculty were pseudomonas aeruginosa, Staphylococcus aureus and Streptococcus, respectively, and after disinfection, the most common types of microorganisms were Pseudomonas aeruginosa, Staphylococcus aureus and Enterococcus, respectively.Conclusion: In general, this study showed that the disinfection method of units in dental faculty can not reduce the severity of infection of the units. Given what was stated, it is recommended that the method and the substances used to disinfect the unit to be changed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".