Methicillin-resistant <i>Staphylococcus</i> aureus nasal swabs: trends in use and association with outcomes
Bibliographic record
Abstract
Abstract Objective: To investigate patterns of early methicillin-resistant Staphylococcus aureus (MRSA) nasal swab use in US hospitals and the association with de-escalation of MRSA-specific antibiotics. Design: Retrospective cohort study. Setting: PINC-A1 Healthcare Database (2008–2021). Participants: Adults with sepsis present on admission who received invasive mechanical ventilation by hospital day 1. Methods: We assessed interhospital variation and time trends in early polymerase chain reaction-based MRSA nasal swab use using bivariable regression. Next, we used competing risks multivariable regression to assess the association of early (started by hospital day 2) anti-MRSA antibiotic duration with care in a high (≥90%) versus low (<10%) swab use hospital. Results: We included 699,474 patients across 788 hospitals to evaluate trends in early swab use; 151,205 (21.6%) received a swab. Use of swabs varied across hospitals (median use: 6.0% [interquartile range: 0–37.6%; full range: 0%–98.0%]; median odds ratio [95% CI]: 84.7 [63.3–115.6]) and overall use increased over time (3.5% in 2008 quarter 1 increasing to 29.5% in 2021 quarter 4; regression coefficient [95% CI]: 0.14% [0.12%–0.15%]). Considering 41,599 patients (9,796 [23.6%] in 33 hospitals where ≥90% received swabs and 31,763 [76.4%] in 67 hospitals with <10% use), anti-MRSA antibiotic durations were shorter in hospitals where ≥90% (vs < 10%) received a swab (adjusted sub-hazard ratio for discontinuation of antibiotics [95% CI]: 1.17 [1.04–1.31], P = .007). Conclusions: Use of early polymerase chain reaction-based MRSA nasal swabs varied across US hospitals and increased over time. Receiving care in a hospital with higher swab use was associated with shorter anti-MRSA antibiotic duration.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".