Screening of Burns Unit Staff of a Tertiary Care Hospital for Methicillin-Resistant Staphylococcus Aureus Colonisation
Bibliographic record
Abstract
Staphylococcus aureus is a significant nosocomial pathogen and the development of resistance to methicillin poses a major threat to its control. This study was conducted over a three month period in a Burns Unit of a tertiary care hospital to determine the prevalance of methicillin- resistant S. aureus (MRSA) colonisation in health care workers. All health care workers were screened using swabs from the hairline, nostril, axilla, and hands. Seventeen of 34 health care workers screened were MRSA-positive; 16 people tested positive for the methicillin-sensitive strain of S. aureus, 7 of whom were also MRSA-positive at a different site. In total, over two thirds of all health care workers were colonised by S. aureus. Pus samples from patients admitted in the same unit over the three month study period were analysed and showed that 21% of patients were infected or colonised with MRSA. Although a direct causal relationship is not established by these data, it is reasonable to assume that transmission from colonised health care workers is responsible, at least in part, for the extent of infection/colonisation among patients. These findings identify the need for a well defined policy for screening health care workers and controlling the rates of colonisation with potentially dangerous pathogens given the risk of transmission to susceptible patients.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".