Challenges of Long-Term MRSA Management in a Complex Continuing Care Setting
Bibliographic record
Abstract
monitoring and feedback. Fourth, because glutamate dehydrogenase (GDH) testing does not distinguish toxigenic and nontoxigenic strains, use of this assay as the initial screening test in a 2-step algorithm may result in identification of fecal excretors of nontoxigenic C. difficile who would be isolated (ie, GDH positive, toxin negative). Nontoxigenic C. difficile strains do not cause disease and isolation is not required. A third step NAAT test would be required to confirm carriage of a toxigenic strain. Finally, if detection and isolation of fecal excretors are considered important goals of 2-or 3-step testing algorithms, it should be acknowledged that this is an imperfect detection method. Asymptomatic carriers with no diarrhea, including patients who have recently completed CDI treatment, may shed spores to their skin and the environment. n summary, we found that no patients with an alternative explanation for diarrhea and no recent antibiotic exposure had skin and/or environmental shedding of spores. 1 Based on this finding, we believe that it is reasonable to limit testing of such patients, particularly in facilities using stand-alone NAATs for CDI testing. However, our finding that antibiotic-exposed patients with <3 unformed stools within 24 hours who tested positive by NAAT frequently had skin and/or environmental contamination validates some of the concerns raised regarding restricting testing for all patients with unformed stool but not meeting criteria for clinically significant diarrhea. Testing of such patients using a 2-or 3-step algorithm may be helpful to identify fecal excretors who can be isolated to prevent transmission. Finally, because all CDI testing methods have limitations, it is essential that clinicians and infection control practitioners understand the advantages and disadvantages of the laboratory method used in their facility and appreciate the need to correlate test results with clinical assessments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".