TITLE: Bleach versus Accelerated Hydrogen Peroxide for Clostridium difficile and Norovirus Disinfection: A Review of the Clinical Effectiveness and Safety
Bibliographic record
Abstract
Nosocomial infections are a major cause of patient morbidity and mortality, and result in significant costs to the healthcare system. 1-3 Clostridium difficile and noroviruses are commonly occurring pathogens in healthcare facilities, and infection control programs are often put in place to limit the spread of these and other infections. 4 An estimated 20 % to 40 % of nosocomial infections are attributed to cross-infection by way of the hands of health care personnel, and contamination of the hands can occur by either touching patients or contaminated environmental surfaces. 1 As such, environmental cleaning is one of the interventions used to control the spread of infectious organisms in healthcare facilities. A Canadian study found that the products used in cleaning and disinfection of C. difficile in acute care hospitals varies considerably across the country, and that two products that are often used for disinfection in these facilities are bleach (sodium hypochlorite) and accelerated hydrogen peroxide. 5 The relative effectiveness of these disinfectants in controlling pathogens should be considered in developing policy for environmental cleaning. Given that these disinfectants must be used by hospital workers who are subjected to frequent exposure, as well as near patients whose health is already compromised, relative safety must also be a consideration.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".