Prevalence of risk factors for carbapenem-resistant Klebsiella pneumoniae in hospitalized patients. Systematic review
Bibliographic record
Abstract
Introduction. The cases of infection for Carbapenem-Resistant Klebsiella pneumoniae (CRKP) have increased. The patients with infection for Carbapenem-Resistant Klebsiella pneumonia have a terrible forecast and a high mortality rate. Objective. We determine the prevalence of risk factors in patients hospitalized for CRKP. Methods. We perform a search bibliographic of the literature in Pub Med, MEDLINE, and SCOPUS, to December 13, 2021. This systematic review included observational studies on risk factors in patients hospitalized for CRKP. We evaluated the quality methodologic of articles on the base of the Newcastle Ottawa Scale (NOS) of nine stars. Results. we observed that the majority were male (62.90%), the average age was 61 years, and 883 patients with CRKP were counted from the different studies. The main risk factors for CRKP were previous hospitalizations (67.9%), previous use of carbapenems and ß-lactam/ß-lactamase inhibitor (49.59% and 45.49%, respectively), previous venous catheterization (44.99%) and previous stay in the intensive care unit (ICU) (42.9%). Conclusions. In this systematic review, we was the conclusion that the prevalence of risk factors of CRKP in patients hospitalized are previous hospitalizations, use last of antibiotics as carbapenems, ß-lactams/inhibitors, ß-lactamase, previous procedures as central venous catheterization and earlier stays in intensive care units (ICU). These findings can promote the prevention of CRKP infections and rational control over the use of antibiotics from the Ministry of Health towards the general population.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".