Triaging urological surgeries to cope with the coronavirus-19 pandemic
Bibliographic record
Abstract
PURPOSE OF REVIEW: The coronavirus-19 (COVID-19) pandemic has led to strains on hospital resources and difficulties in safely and effectively triaging surgical procedures. In this article, we discuss the important considerations for triaging urologic surgeries during a global pandemic, mitigating factors on how to perform surgeries safely, and general guidelines for specific surgeries. RECENT FINDINGS: Many urological procedures have been cut back due to the pandemic, with benign disease states being most affected whereas oncology cases affected least. Current recommendations in urology triage life-threatening conditions, or conditions that may lead to life-threatening ailments as a priority for treatment during the pandemic. Additionally, published recommendations have been put forth recommending all surgical patients be screened for COVID-19 to protect staff, prevent disease dissemination, and to educate patients on worse outcomes that can occur if infected with COVID-19 in the postoperative period. SUMMARY: COVID-19 has caused worldwide shortages of healthcare resources and increased the need to ethically triage resources to adequately treat the urologic community. These resource limitations have led to increased wait times and cancellations of many urology surgeries that are considered 'elective'.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".