Decontamination Strategies for Control of Environmental Clostridium difficile (Decontamination and C. difficile)
Bibliographic record
Abstract
Clostridium difficile is a global nosocomial pathogen associated with increased morbidity and mortality particularly with the emergence of the hypervirulent strains. C. difficile is ubiquitously present in the environment due to contamination by the excreta of humans and animals. In the hospital environment C. difficile can be isolated from about 30% patients receiving antibiotics or those who are hospitalized. C. difficile spores are not only resistant to antibiotics, but they can also resist the harsh environmental conditions for longer times and thereby facilitate the spread of C. difficile infection (CDI). The primary mode of transmission of the disease is via the feco-oral route as symptomatic patients shed a large number of the pathogen resulting in contamination of the environmental surfaces. CDI occurs in patients with certain risk factors and who acquire the pathogen by ingestion or via contaminated equipments. In a hospital setting, outbreaks can occur in hospitals, nursing homes, and other extended-care facilities due to C. difficile. Therefore strategies to reduce the contamination of C. difficile in the environment will be of prime importance to every health care facility. In this review, the environmental reservoirs of C. difficile, the transmission and the risk factors of CDI are briefly described and the decontamination strategies for containing the pathogen are elaborated.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".