Usefulness of Previous Methicillin‐resistant <i>Staphylococcus aureus</i> Screening Results in Guiding Empirical Therapy for <i>S Aureus</i> Bacteremia
Bibliographic record
Abstract
BACKGROUND: Staphylococcus aureus bacteremia (SAB) is an important infection. Methicillin-resistant S aureus (MRSA) screening is performed on hospitalized patients for infection control purposes. OBJECTIVE: To assess the usefulness of past MRSA screening for guiding empirical antibiotic therapy for SAB. METHODS: A retrospective cohort study examined consecutive patients with confirmed SAB and previous MRSA screening swab from six academic and community hospitals between 2007 and 2010. Diagnostic test properties were calculated for MRSA screening swab for predicting methicillin resistance of SAB. RESULTS: A total of 799 patients underwent MRSA screening swabs before SAB. Of the 799 patients, 95 (12%) had a positive and 704 (88%) had a negative previous MRSA screening swab. There were 150 (19%) patients with MRSA bacteremia. Overall, previous MRSA screening swabs had a positive likelihood ratio of 33 (95% CI 18 to 60) and a negative likelihood ratio of 0.45 (95% CI 0.37 to 0.54). Diagnostic accuracy differed depending on mode of acquisition (ie, community-acquired, nosocomial or health care-associated infection) (P<0.0001) and hospital (P=0.0002). At best, for health care-associated infection, prior MRSA screening swab had a positive likelihood ratio of 16 (95% CI 9 to 28) and a negative likelihood ratio of 0.27 (95% CI 0.17 to 0.41). CONCLUSIONS: A negative prior MRSA screening swab cannot reliably rule out MRSA bacteremia and should not be used to guide empirical antibiotic therapy for SAB. A positive prior MRSA screening swab greatly increases likelihood of MRSA, necessitating MRSA coverage in empirical antibiotic therapy for SAB.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".