Navigating urology’s new normal and mitigating the effects of a second wave of COVID-19
Bibliographic record
Abstract
The initial wave of the COVID-19 crisis forced immediate and seismic changes on urological practice, patient care, and education — collateral damage to the upending of societal and global economic norms. Lockdowns and limitations curtailed access to the physical spaces of the clinic and operating room, and slashed remuneration secondarily. As the curves flattened and healthcare infrastructure was deemed secure, we have begun opening our societies and clinical lives again. Remote care, in particular, has remained the default model of care, with attendant changes in how urological experience and education are obtained. As the colder weather looms, so does uncertainty about repeated waves of infection, the sustainability of the businesses that sustain our economy and the ability to provide high-quality, uninterrupted care outside of emergencies. To this end, we have compiled perspective and advice from previous authors and contributors to the CUA and CUAJ’s educational and research output, with a view to the future, to second waves, and ever-altered clinical landscapes.
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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.020 | 0.019 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.020 | 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".