Surveillance Cultures for Hospital‐Associated Infections
Bibliographic record
Abstract
Abstract Antibiotic and antifungal resistance (AR)—the ability of bacteria and fungi to defeat the drugs designed to kill them—is one of the greatest global public health challenges. Antibiotics and antifungals are among our most powerful tools for fighting life‐threatening infections. The threat of AR undermines progress in health care, food production, and life expectancy. The Centers for Disease Control and Prevention (CDC) is a leader in the fight against this global threat, driving aggressive action along with partners and facilitating collaborative responses to resistance through a One Health approach. State and local health care‐associated infection (HAI)/AR programs are essential to U.S. efforts to detect, prevent, respond to, and contain HAI and AR pathogens. In 2019, CDC evaluated the sensitivity and specificity of many carbapenem‐resistant Enterobacterales and Pseudomonas aeruginosa antimicrobial susceptibility testing phenotypes to determine which were most predictive of the presence of carbapenemase genes.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".