Bacterial Contamination in Intensive Care Unit at Al-Imam Al-Hussein Hospital in Thi-qar Province in Iraq
Bibliographic record
Abstract
Cross- infection from patient to patient or from hospital personnel to patients represents constant hazards. It is one of the most important causes of morbidity and mortality especially in Intensive Care Unit all over the world. To identify the types and the source of bacterial contamination in ICU and to study the sensitivity of bacterial isolates to commonly used antibiotics in hospitals this study had been conducted in Al-Imam Al-Hussein hospital in Thi-qar province for the period from the 1st of September to the end of December 2011. A total of 320 swabs and samples were collected from 17 different sites of Intensive Care Unit environment and inoculated on a normal cultural media, then incubated at 37°C for 24 hour. The obtained growth revealed different bacterial colonies which had been tested for their morphological and biochemical characteristics. Sixty eight of pure isolates were obtained including 24 (35.29%) Gram positive bacterial isolates, and 44(64.71%) of Gram negative bacterial isolates, the highest rates (19.11%) of bacterial contamination had been found on the walls and the floor. Sensitivity tests for all isolates were done using 25 types of commonly used antibiotics in Iraq, among Gram negative bacteria and gram positive bacteria the genus Enterobacter spp and Staphylococcus spp respectively, showed the highest resistance to most of the tested antibiotics, MIC tests for 5 types of antibiotics being applied for the most resistant and the most sensitive isolates had identified that all isolates have a low rate of MIC against Ciprofloxacine. Bacillus spp and Enterobacter spp were the most prevalent bacterial contaminants of Intensive Care Unit environment .such contamination could be managed mostly by strict application of sterilization measures.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".