bacteria [letter]. Emerg Infect Dis. 2011 Jun; [Epub ahead of print] Bedbugs as Vectors for Drug-Resistant Bacteria
Bibliographic record
Abstract
resurgence of bedbugs (Cimex lectularius) has occurred (1). Although the basis for this resurgence is unclear, large bedbug infestations have been attributed to increased worldwide travel, altered insecticide management, and increased resistance to pesticides (2). Marginalized populations in large urban centers appear to be disproportionately affected; ≈30 % of shelters in Toronto, Ontario, Canada, report bedbugs (1). We report recovery of methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococcus faecium (VRE) from bedbugs in Vancouver, British Columbia. Three patients, all residents of Vancouver’s Downtown Eastside—an impoverished community in Vancouver with high rates of homelessness, poverty, HIV/AIDS, and injection drug use (3)—were hospitalized and found to be infested with bedbugs. Hypothesizing that these parasites may be vectors for the transmission of antimicrobial drug–resistant pathogens, we collected 5 bedbugs and tested them for drug-resistant organisms. The bedbugs were homogenized and streaked onto standard microbiological media, including 5 % sheep’s blood agar. Bacterial colonies were identified by using conventional and automated microbiological methods, and susceptibility testing was performed in accordance with Clinical Laboratory
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.047 | 0.015 |
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".