High Rates of Staphylococcus aureus
Bibliographic record
Abstract
Members of the Northern Antibiotic Resistance Partnership are listed at the end of this article. Surveillance of Staphylococcus aureus infections in 3 northern remote communities of Saskatchewan was undertaken. Rates of methicillin-resistant infections were extremely high (146–482/10,000 population), and most (98.2%) were caused by USA400 strains. Although USA400 prevalence has diminished in the United States, this strain is continuing to predominate throughout many northern communities in Canada. Over the past decade, community-associated methicillin-resistant Staphylococcus aureus (CA-MRSA) infections have rapidly emerged in Canada (1). These CA-MRSA strains are causing infections in often young otherwise healthy persons with no traditional health care– associated risk factors (2), linked with increased illness severity and deaths (3), and now entering and being disseminated within health care facilities (4). In comparison to the incidence of CA-MRSA infections in large urban centers across Canada, which has been addressed through the ongoing efforts of the Canadian Nosocomial Infection Surveillance Program (1), little attention Page 1 of 8has been directed at the emerging problem of CA-MRSA or CA-methicillin-susceptible S.
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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".