Urology Amidst the War on COVID-19
Bibliographic record
Abstract
Objectives: We sought to review the impact of the COVID-19 pandemic on the practice of urology internationally, with particular focus on the Australian response. Methods: A literature search of PubMed was conducted using search terms “urology,” “coronavirus,” “COVID-19,”and “surgery.” This generated 165 articles. The abstracts were reviewed for relevance, and 33 articles were selected, reviewed in depth, and information synthesised along with relevant government, surgical college, and urological society policy documents. Results: Extensive health care changes have been implemented worldwide to curb infection rates. Elective surgery cancellations have been widely mandated to curb infection rates with mixed success. Whilst demand on hospital resources was reduced by up to 80%, the estimated cost to clear the surgical backlog in the UK has reached £100 million. Strict perioperative precautions have also been employed with mandatory personal protective equipment for all surgical staff and guidelines fast tracked for safe aerosol-generating procedures. Attempts to reduce exposure to patients and health care workers resulted in compromised operative time, blood loss, and length of hospital stay, with potential increased risk of short- and long-term complications. Systemic changes to education and training have also been made. Clinically, the cancellation of training examinations and a freeze on rotations and elective surgery restrictions have blunted surgical experience and teaching. The effect has rippled through junior doctor positions, with uncertainty remaining for training positions in 2021. Conclusions: The COVID-19 pandemic is the greatest current challenge facing health care worldwide. Amidst elective surgery restrictions, novel preoperative testing procedures and intraoperative precautions, providing safe and appropriate urological care is a major challenge. This review was derived entirely from expert opinion articles. Further research into the virus is needed to bring the world safely through the pandemic, and post-pandemic recovery will likely be the next challenge.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".