Why do susceptible bacteria become resistant to infection control measures? A <i>Pseudomonas</i> biofilm example
Bibliographic record
Abstract
for patients with suspected or proven respiratory viral infection.This protocol would err on the side of caution in an attempt to mitigate the risk of transmission to healthcare workers and others."The Centers for Disease Control and Prevention (CDC), the Association of periOperative Registered Nurses (AORN), the Occupational Safety and Health Administration (OSHA), and others recommend similar protective measures: to use "(m)ask and goggles or a face shield : : : Use during patient care activities likely to generate splashes or sprays of blood, body fluids, secretions, or excretions."Incidence data demonstrate that guidance is neither protective nor prescriptive enough.Because most mucus membrane exposures occur to the eyes and because eye protection use is low (2.8%-12.8%),more specific guidance needs to include use not only "when splashes or sprays are likely" but also with elements of measure, control, and surveillance (occupational health, environmental health and safety, industrial hygiene, employee health, infection prevention, etc. rounds).Healthcare employers should improve availability and accessibility of protective eyewear in patient, exam, and procedure rooms, similar to including infection prevention and control caddies (gloves, gowns) for transmission-and contact-based or isolation precautions.Given the increasing prevalence in patients with coinfection of human immunodeficiency virus (HIV) and hepatitis C virus (HCV), hepatitis B virus (HBV), tuberculosis (TB), and multidrug-resistant organisms (MDROs) such as MRSA, protecting healthcare personnel is more critical than ever.8-10 A single eye exposure can result in transmission of 1 or more pathogenic organisms that can result in occupational illness or infection.
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.039 | 0.029 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".