Impact of COVID-19 on Urology Practice: A Global Perspective and Snapshot Analysis
Bibliographic record
Abstract
The global impact of the 2019 novel coronavirus disease (COVID-19) pandemic on urology practice remains unknown. Self-selected urologists worldwide completed an online survey by the Société Internationale d'Urologie (SIU). A total of 2494 urologists from 76 countries responded, including 1161 (46.6%) urologists in an academic setting, 719 (28.8%) in a private practice, and 614 (24.6%) in the public sector. The largest proportion (1074 (43.1%)) were from Europe, with the remainder from East/Southeast Asia (441 (17.7%)), West/Southwest Asia (386 (15.5%)), Africa (209 (8.4%)), South America (198 (7.9%)), and North America (186 (7.5%)). An analysis of differences in responses was carried out by region and practice setting. The results reveal significant restrictions in outpatient consultation and non-emergency surgery, with nonspecific efforts towards additional precautions for preventing the spread of COVID-19 during emergency surgery. These restrictions were less notable in East/Southeast Asia. Urologists often bear the decision-making responsibility regarding access to elective surgery (40.3%). Restriction of both outpatient clinics and non-emergency surgery is considerable worldwide but is lower in East/Southeast Asia. Measures to control the spread of COVID-19 during emergency surgery are common but not specific. The pandemic has had a profound impact on urology practice. There is an urgent need to provide improved guidance for this and future pandemics.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".