A Multi-Centre Snapshot Study Comparing Acute Urological Admissions during the COVID-19 Lockdown to a pre-COVID Period
Bibliographic record
Abstract
Introduction COVID-19 has had a devastating effect around the globe with over 560,000 deaths and 12.8 million people now infected as of 13 July according to WHO, 2020. Our study looked at the pandemic’s effect on acute admissions across two institutions compared to the same time (23 March to 30 April) in 2019. Method We collected data using records from the hospital’s coding department, analysed patients discharge letters, and grouped patients by their final diagnoses. We also looked at variances in daily acute admission numbers. Statistical analysis was performed using the Chi-squared test and descriptive statistics. Results One hundred seventy-six patients were admitted in 2019 and 92 patients in 2020. There was a 58% significant reduction in acute admissions in 2020 (p<0.0000226). Five (5.43%) patients died in 2020 compared to four (2.27%) in 2019, and the most common presentation was renal colic, 23% rising to 29% in 2020. Conclusion There was a significant reduction in acute urological admissions during the UK lockdown period. Possibly as a consequence, the mortality rate doubled. Further analysis with larger cohorts is recommended for future studies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".