Age-stratified perioperative mortality after urological surgeries
Bibliographic record
Abstract
INTRODUCTION: More elderly patients are presenting for surgical consultation. Understanding the risk of mortality by age group after urological surgery is important for patient selection and counselling. METHODS: A historical cohort study of The American College of Surgeons National Surgical Quality Improvement Program (NSQIP) database from 2006-2015 was performed. Current procedural terminology (CPT) codes for similar surgical procedures were grouped for analyses. Urological procedures commonly performed in elderly patients were identified and stratified by patient age and surgical approach (open vs. laparoscopic/robotic). The primary outcome was the absolute risk of death by 30 days stratified by age for each surgical procedure. The secondary outcome was risk of death by surgical approach (open vs. laparoscopic/robotic). RESULTS: Twelve urological procedures were reviewed including 124 262 patients. A total of 1011 (0.8%) deaths occurred by 30 days after surgery. The procedure with the highest incidence of mortality by 30 days was open nephroureterectomy (2.9 %). In patients 80 years and over, the procedure with the highest incidence of death was open radical nephrectomy (5.32%). There was an increased risk of mortality with increasing age group for all procedures. Unadjusted risk of mortality was consistently higher in patients who receive open compared to laparoscopic surgery. CONCLUSIONS: There is an increasing risk of mortality with age and with open surgical approach in urology. Knowledge regarding the absolute risk of mortality in patients receiving common urological surgeries may improve patient selection and counselling.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".