Ten-Year Sepsis Rates Comparing Extracorporeal Shock Wave Lithotripsy and Ureterorenoscopic Laser Lithotripsy in an Australian Population
Bibliographic record
Abstract
ObjectivesTo compare the rate and predictors of septic complications after shock wave lithotripsy (SWL) and flexible ureteroscopy and laser lithotripsy (FURS) in an Australian population.MethodsHospital admission data were extracted from the Victorian Admitted Episodes Dataset (VAED) regarding all elective admissions for SWL and FURS for treatment of intrarenal stones from 2009 to 2018, inclusive. Sepsis was defined by the ICD-10 diagnostic code, A41.ResultsThere were 13 154 inpatient episodes analysed, comprising SWL (6033) and ureterorenoscopic laser lithotripsy (7121). Males made up 67.43% of SWL patients and 63.34% of FURS patients. Median age was 57 years in both groups. Median American Society of Anesthesiologists physical status classification grade (ASA grade) was 2 for both groups, but proportionally more FURS patients were ASA grade 3 to 4 (P < 0.001). Postoperative sepsis was more common in the FURS group (1.43% vs. 0.03%), as was intensive care unit admission (1.00% vs. 0.10%). Average length of stay was longer for FURS (1.43 days vs. 1.06 days). There were 4 inpatient deaths, all from the FURS group. FURS procedure, female sex, and a higher ASA grade were each independent predictors of sepsis.ConclusionsFURS may have a significantly higher relative risk of postoperative sepsis than SWL in high-risk patients as determined in this study. While overall risk is low, higher comorbidity (ASA grade 3 or 4) and female sex were independent predictors of sepsis. For these patients in particular, and when clinically appropriate, SWL may be considered as a potentially safer alternative to FURS.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".