Hospital‐Acquired Methicillin‐Resistant <i>Staphylococcus aureus</i>: Epidemiology, Treatment and Control
Bibliographic record
Abstract
Antimicrobial‐resistant organisms are an expanding problem, resulting in increased morbidity and mortality, prolonged hospital stay, and heightened health care costs for care and antimicrobial management. Methicillin‐resistant Staphylococcus aureus (MRSA) has emerged as a major hospital‐acquired, antimicrobial‐resistant pathogen. MRSA not only colonizes hospitalized patients but has a propensity to produce more serious, life‐ threatening infection than methicillin‐susceptible strains. Numerous risk factors, including antimicrobial use and proximity to a patient harbouring MRSA, have been linked to the acquisition of MRSA. Although vancomycin has been the mainstay of therapy for MRSA, failures have been reported due to reduced susceptibility to this agent. Other available therapeutic agents for MRSA include trimethoprim‐sulfamethoxazole, tetracycline, fusidic acid, rifampin (in combination with other effective agents) and linezolid. Potential therapeutic agents that are currently under investigation include daptomycin, dalbavancin, tigecycline, ceftobiprole and iclaprim. Only enhanced infection control practices can halt the progressive transmission of MRSA in the hospital environment. However, such measures have not quite fulfilled their promise in clinical studies. Moreover, eradication of MRSA colonization is controversial and may promote greater resistance. A multidisciplinary approach to the prevention, containment and treatment of MRSA is necessary.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".