Comparative study of the incidences of hospitalinfections in the burn department: the years2015 vs. 2022
Bibliographic record
Abstract
Introduction: Patients with burn injuries are particularly vulnerable to nosocomial infections due to the loss of the skin's protective barrier.Despite the improvement in the survival rates of burn patients in recent years, nosocomial infections are still a significant problem and are a significant threat to the treatment process.Material and methods: A retrospective comparative cohort study was conducted.A comparative analysis was made of the medical records of patients hospitalized at the burn unit, hospitalized in 2015 and 2022, who had a nosocomial infection.Results: In 2022, a statistically significant, higher percentage of patients with nosocomial infection was recorded compared to 2015 (chi-square (χ 2 ) = 11.635,p < 0.05).Both in 2015 and 2022, the dominant bacteria were Gram (-) Acinetobacter baumannii.In the case of bacteria (Klebsiella pueumoniae) a statistically significant decrease in infections caused by this pathogen was found in 2022 compared to 2015 (χ 2 = 4.3835, p < 0.05).In 2015, the dominant Gram (+) bacteria were Staphylococcus aureus MRSA, while in 2022 a statistically significant decrease in infections with this strain was noted (χ 2 = 78.729,p < 0.05).The dominant type of fungi in 2015 was Candida albicans, while in 2022 a statistically significant decrease in the proportion of this pathogen was recorded (χ 2 = 41.7053,p < 0.05).In 2015, Gram (-) had the largest share in isolated pathogens, and in 2022 there was a significant decrease in their proportion in relation to the total (χ 2 = 14.301, p < 0.05).In 2022 there was a statistically significant increase in the proportion of Gram (+) compared to 2015 (χ 2 = 21.185,p < 0.05).Conclusions: Nosocomial infections are still a significant problem in the treatment of patients after burn injury, and they require constant supervision and close control.Over the period of 7 years, there was a significant change in the dominant pathogens responsible for the occurrence of nosocomial infections in the burn units.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".