Dynamics of acquisition and loss of carriage of Staphylococcus aureus strains in the community: The effect of clonal complex
Bibliographic record
Abstract
BACKGROUND: Staphylococcus aureus nasal carriage increases infection risk. However, few studies have investigated S. aureus acquisition/loss over >1 year, and fewer still used molecular typing. METHODS: 1123 adults attending five Oxfordshire general practices had nasal swabs taken. 571 were re-swabbed after one month then every two months for median two years. All S. aureus isolates were spa-typed. Risk factors were collected from interviews and medical records. RESULTS: 32% carried S. aureus at recruitment (<1% MRSA). Rates of spa-type acquisition were similar in participants S. aureus positive (1.4%/month) and negative (1.8%/month, P = 0.13) at recruitment. Rates were faster in those carrying clonal complex (CC)15 (adjusted (a)P = 0.03) or CC8 (including USA300) (aP = 0.001) at recruitment versus other CCs. 157/274 (57%) participants S. aureus positive at recruitment returning ≥ 12 swabs carried S. aureus consistently, of whom 135 carried the same spa-type. CC22 (including EMRSA-15) was more prevalent in long-term than intermittent spa-type carriers (aP = 0.03). Antibiotics transiently reduced carriage, but no other modifiable risk factors were found. CONCLUSIONS: Both transient and longer-term carriage exist; however, the approximately constant rates of S. aureus gain and loss suggest that 'never' or truly 'persistent' carriage are rare. Long-term carriage varies by strain, offering new explanations for the success of certain S. aureus clones.
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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 | 0.008 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".