Has the SARS-CoV-2 Pandemic Improved the Management of Acute Ureteric Colic?
Bibliographic record
Abstract
Objective The WHO declared SARS-CoV-2 a pandemic on 11th March 2020 prompting a rapid change to surgical practice. This study focuses on how the management of ureteric colic has adapted in a major tertiary referral unit during the peak of the pandemic so that lessons be can be learned in case a second wave occurs. Materials and Methods We compared admission rates and treatment patterns against national and European guidelines in 20 weeks, divided into pre- and peri-pandemic. Results A total of 72 patients were admitted during the study period. 64% (46/72) were admitted pre-pandemic. 22% (10/46) of these were septic (5 stented, 5 nephrostomized) while 20% (9/46) were managed conservatively. 59% (27/46) of pre-pandemic admissions were suitable for active treatment, of which 48% (13/27) received definitive treatment (11 ureteroscopy (URS), 2 shockwave lithotripsy (SWL)) all within 48 hours of admission. 52% (14/27) had temporising procedures (11 stented, 3 nephrostomized) and underwent definitive treatment within 63 days. Of the total patients, 36% (26/72) were admitted peri-pandemic. 23% (6/26) were septic (1 stent, 5 nephrostomized), while 31% (8/26) were managed conservatively. 46% (12/26) were suitable for active treatment. 75% (9/12) received definitive treatment (4 URS, 5 SWL) of which 33% (4/12) within 48 hours and the remaining treated and stone free within 12 days. 25% (3/12) had temporising procedures (2 stented, 1 nephrostomized), with the definitive treatment provided within 17 days. Conclusion Ureteric colic admissions were reduced by almost half during the pandemic. There has been increased primary treatment with a reduction in temporising procedures and time to receiving definitive treatment. In the ‘new normal,’ lessons learned must be carried forward to maintain high rates of definitive treatments.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".