Healthcare surfaces and transmission of pathogens: A consensus statement
Bibliographic record
Abstract
Approximately 1.7 million infections occur annually in U.S. hospitals, with one in 25 patients acquiring a healthcare-associated infection (HAI) while hospitalized. Ongoing improvements in infection prevention and control protocols and processes reduce the risk of HAIs, but these are inhibited by surface pathogens that persist after routine cleaning and disinfection. It is unknown whether various surfaces and products in the healthcare setting are damaged by disinfectants creating invisible microbial reservoirs and ultimately, increasing the risk of transmitting pathogens. There are a variety of guidelines and recommendations to ensure equipment and surfaces can be cleaned and disinfected for safe use in the clinical setting, but no uniform approach exists for testing and product claims. Additionally, the life cycle of built environment surfaces and assembled clinical equipment may be greatly shortened by surface degradation, impacting cost and organizational sustainability goals. This consensus paper was developed to highlight gaps in evidence and recommend future action to a variety of stakeholders.
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 | 0.085 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.010 | 0.011 |
| Research integrity | 0.029 | 0.034 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".