Prévalence des infections nosocomiales d’origine microbienne au service de réanimation du Centre hospitalo-universitaire de Annaba (Algérie)
Bibliographic record
Abstract
Nosocomial infections constitute a significant public health problem but are poorly controlled in our health structures, especially those associated with resuscitation care. The first objective of this study was to identify the different microbial strains present in different biological samples taken from patients staying in the resuscitation unit of the Annaba University Hospital Center. The second objective was to assess the antimicrobial sensitivity of isolated microbes from the patients’ samples, to determine the risk factors, the most incriminated microbial agents in nosocomial infections. During the study period from January 2013 to December 2016, we collected 1,151 biological samples from 1,938 patients admitted to Resuscitation Medical Service. The samples were subjected to different microbiological analyses. Our results showed that over 59% of the collected samples were microbiologically positive. The identified species include Candida albicans (115 cases) and Candida.sp (81 cases). The Gram-negative bacterial strains found in the samples included Acinetobacter baumannii (108 cases), Klebssiella pneumoniae (99 cases) Pseudomonas aeruginosa (79 cases), and Escherichia coli (73 cases). Gram positive bacteria included Staphylococcus aureus (94 cases) and Enterococcus faecalis (53 cases). The antibiogram analyses showed significant antibiotic resistance reaching 93.75% for ampicillin, but sensitivity to colistin reaching 81.81%. Moreover, the fungal strains are represented by the genus albicans, showing a significant resistance to antifungals, reaching 80% with miconazole. Conclusion. The nosocomial infections in the medical unit were caused by the candida genus and multi-resistant bacteria to various antibiotics and antifungals. The most important factor associated with these infections was the use of medical devices.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".