The Urologist, Personal Protective Equipment (PPE) and COVID-19
Bibliographic record
Abstract
Background and Objective To review the literature from a urologist’s perspective regarding the use of Personal Protective Equipment (PPE), associated challenges, and other potential measures that can be taken to reduce the risk of nosocomial COVID-19 transmission. Material and Methods A literature review using PubMed, Cochrane Review, and Google Scholar database search was performed using the keyword terms “COVID-19”, “Coronavirus”, “Personal Protective Equipment” (PPE), “healthcare workers” (HCW), “protection”, “masks”, and “urology”. Non-English articles were excluded. We present a summary of key guidance provided by regulatory bodies as well as some of the key articles published to date relating to PPE. Discussion SARS-CoV-2 virus is found mainly in the respiratory system but is also in blood, feces, semen, and urine. Both standard infection control precautions (SICPs) and transmission-based precautions (TBPs) are necessary to reduce nosocomial transmission of COVID-19 infection. PPE includes gowns, gloves, masks or respirators, goggles, and face shields; however, wearing PPE is only part of many precautionary measures that are necessary to prevent viral transmission. When used appropriately PPE not only protects HCWs from patients but also protects patients from HCWs who may be asymptomatic carriers of COVID-19 infection. Attention should also be paid to fit testing and fit checking, donning and doffing, and ever-evolving guidelines on PPE. Wearing PPE for a long time is also technically challenging and may adversely affect surgical outcomes. Shortages of PPE in the supply chain during the peak of the pandemic as well as concerns about substandard PPE should be considered for a possible second wave of COVID-19. Other key measures to minimize nosocomial SARS-CoV-2 virus transmission are a symptom and temperature screening of patients and staff; controlling the flow of patients, staff, and relatives in hospitals; self-isolation by patients before elective surgery; a robust testing protocol for both patients and staff; patient and staff cohorting; physical distancing; good hand hygiene; respiratory etiquette including face coverings for patients, staff and visitors; proper disposal of waste and enhanced cleaning; thorough cleaning and sterilization of surgical equipment performed post-operatively; choosing suitable anesthetic methods to minimize aerosolization of the virus; and if possible ensuring a negative-pressure theatre environment while dealing with COVID-19 positive patients. As scientific and regulatory bodies continue to issue updated guidance as more data is collected and a better knowledge base is developed regarding COVID-19 employers and staff need to keep up to date with guidance also. Conclusion COVID-19 will be around for the foreseeable future and infection rates may fluctuate as restrictions are eased. HCWs including urologists should take appropriate PPE measures not only in theatres, clinics, and endoscopy suits but also when performing simple tasks such as urine dipsticks, catheter, nephrostomy management, digital rectal examination (DRE), prostate biopsies, etc. as SARS-CoV-2 can be detected in feces, urine, and semen. Both employers and HCWs should adhere strictly to current guidelines and work together to minimize nosocomial transmission of COVID-19 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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".