Using MRSA Screening Tests To Predict Methicillin Resistance in Staphylococcus aureus Bacteremia
Bibliographic record
Abstract
Bloodstream infections with Staphylococcus aureus are clinically significant and are often treated with empirical methicillin resistance (MRSA, methicillin-resistant S. aureus) coverage. However, vancomycin has associated harms. We hypothesized that MRSA screening correlated with resistance in S. aureus bacteremia and could help determine the requirement for empirical vancomycin therapy. We reviewed consecutive S. aureus bacteremias over a 5-year period at two tertiary care hospitals. MRSA colonization was evaluated in three ways: as tested within 30 days of bacteremia (30-day criterion), as tested within 30 days but accounting for any prior positive results (ever-positive criterion), or as tested in known-positive patients, with patients with unknown MRSA status being labeled negative (known-positive criterion). There were 409 S. aureus bacteremias: 302 (73.8%) methicillin-susceptible S. aureus (MSSA) and 107 (26.2%) MRSA bacteremias. In the 167 patients with MSSA bacteremias, 7.2% had a positive MRSA test within 30 days. Of 107 patients with MRSA bacteremia, 68 were tested within 30 days (54 positive; 79.8%), and another 21 (19.6%) were previously positive. The 30-day criterion provided negative predictive values (NPV) exceeding 90% and 95% if the prevalence of MRSA in S. aureus bacteremia was less than 33.4% and 19.2%, respectively. The same NPVs were predicted at MRSA proportions below 39.7% and 23.8%, respectively, for the ever-positive criterion and 34.4% and 19.9%, respectively, for the known-positive criterion. In MRSA-colonized patients, positive predictive values exceeded 50% at low prevalence. MRSA screening could help avoid empirical vancomycin therapy and its complications in stable patients and settings with low-to-moderate proportions of MRSA bacteremia.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".