Methicillin-Resistant <i>Staphylococcus aureus</i> Infections in Patients With Renal Disorders: A Review
Bibliographic record
Abstract
Methicillin-resistant Staphylococcus aureus (MRSA) infection is a rapidly escalating global health burden. It is not only restricted to patients in the hospital settings but has also rooted deeply in the community settings. With increasing prevalence of life style and kidney diseases, the prevalence of MRSA infections is also expected to rise. MRSA infection plays a major role in renal disorders due to its direct vascular access (VA) thereby making patients undergoing dialysis and renal transplant more vulnerable to infections. Prolonged hospital stay, close proximity to MRSA-infected individual, exposure to broad-spectrum antibiotics, surgery and presence of foreign bodies such as central venous catheters predispose an individual to MRSA infection. Current panel of antibiotic treatment includes vancomycin, teicoplanin, linezolid, daptomycin, tigecycline and ceftaroline. However, emergence of resistant strains and several undesirable features pertaining to safety and tolerability of these drugs have led to limited options available for the management of multidrug-resistant MRSA infection in patients with renal disorders. Therefore, there is an increasing need for developing a new potent antibacterial agent with established renal safety that decreases the mortality and morbidity rates in MRSA-infected renal patients. World J Nephrol Urol. 2019;8(1):8-13 doi: https://doi.org/10.14740/wjnu384
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".