A cross sectional study of animal and human colonization with Methicillin-Resistant Staphylococcus aureus (MRSA) in an Aboriginal community
Bibliographic record
Abstract
Abstract Background Methicillin-resistant Staphylococcus aureus (MRSA) infections are common among humans in Aboriginal communities in Canada, for unknown reasons. Methods Cross sectional study of humans and dogs in an Aboriginal community of approximately 1200 persons. Our objectives were to measure community-based prevalence of nasal MRSA colonization among humans, use multivariable logistic regression to analyze risk factors for MRSA colonization, and perform molecular typing of Staphylococci isolated to investigate interspecies transmission. Results 461 humans were approached for consent and 442 provided complete data. 109/442 (24.7 %, 95 % C.I. = 20.7–28.7 %) of humans were colonized with MRSA. 169/442 (38.2 %) of humans had received antibiotics in the last 12 months. Only number of rooms in the house (OR 0.86, p = 0.023) and recreational dog use (OR 7.7, p = 0.002) were significant risk factors for MRSA colonization. 95/109 (87.1 %) of MRSA strains from humans were of the same spa type (CMRSA10/USA300). 8/157 (5.1 %, 95 % C.I. = 1.7–8.5 %) of dogs were colonized with methicillin-susceptible S. aureus, and no dogs were colonized with MRSA. Conclusions Human MRSA colonization in this community is very common, and a single clone is predominant, suggesting local transmission. Antibiotic use is also very common. Crowding may partially explain high colonization, but most considered risk factors including animal exposure were not predictive. Very few dogs carried human Staphylococcal strains.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".