Urological implications of SARS CoV-19.
Bibliographic record
Abstract
INTRODUCTION: The novel coronavirus disease 2019 (COVID-19), pandemic has afflicted > 3.3 million people around the world since December 2019. Though, more than 1000 publications have appeared in scientific journals addressing a plethora of questions, there is a considerable hiatus in understanding of the behavior and natural history of the virus and its impact on urology. Also, a modified approach is the need of hour in taking care of patients as urologists should safeguard their teams, families, and patients. MATERIAL AND METHODS: The authors have used guidelines from USA, Canada, UK, Europe and India for making recommendations to help urologist define their own policies that may have to be fine-tuned on the basis of continued and evolving challenges they would encounter and the local resources at their disposal. RESULTS: COVID-19 do effect genitourinary system from kidney to testis. The authors provide scientific basis to urologists to help identify patients by remote consultation who are likely to be harmed by coming to the hospital, and not to miss those who need hospitalization for diagnostic or therapeutic interventions. There is uncompromised need of specific precautions during surgery to safe guard the surgeon and his team along with the patient. CONCLUSIONS: Urological operations during COVID-19 pandemic should be limited to emergency cases during the acute phase with an exit strategy planned in a staggered manner, based on the scientific risk stratification. Telemedicine (e-clinics or virtual clinics) would help achieve the goal of risk stratification.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".