Bibliographic record
Abstract
Infections are common in long-term care facilities. The most frequent endemic infections are urinary infection, respiratory infection, and skin and soft tissue infections. Outbreaks also occur frequently, and some facilities have a high prevalence of colonization of residents with antimicrobial-resistant organisms. Our understanding of infections and the development of infection-control programs for long-term care facilities have progressed greatly over the past 15 years. Whereas the occurrence of infections has been described and specific guidelines for infection-control programs in long-term care facilities have been developed, there is still limited evaluation of the effectiveness of programs or specific interventions to support prioritization of infection-control resources. In addition, the spectrum of patients and care delivered in long-term care facilities continues to evolve. Increasingly, chronic care patients, including those requiring chronic respirator therapy, dialysis, or percutaneous feeding tubes, are cared for in these facilities. Our understanding of prevention of infection in these patients remains limited. Important questions include what interventions may prevent endemic infections, what are the most effective means to identify outbreaks early, and what interventions may minimize the prevalence of antimicrobial-resistant organisms. Programs to optimize antimicrobial use need to be developed. Thus, although progress in understanding and practice has been made, important questions remain.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".