Comorbidity and nutritional indices as predictors of morbidity after transurethral procedures: A prospective cohort study
Bibliographic record
Abstract
INTRODUCTION: Preoperative scores are widely used predictors of complications after major surgery. These scores, however, are not widely used in transurethral procedures. The aim of this study was to assess the value of the Charlson Comorbidity Index (CCI), the age-adjusted CCI, the American Society of Anesthesiologist score (ASA) and the Nutritional Risk Score (NRS) in predicting early morbidity after transurethral urological procedures. METHODS: Consecutive patients undergoing transurethral resection of the bladder or the prostate were prospectively enrolled. The scores were calculated preoperatively; 30-day complications were prospectively recorded according to the Dindo-Clavien classification. Univariate logistic regression was performed to investigate the value of each score and of other factors (i.e., age, sex, body mass index, anemia, smoking habit, type of operation and anaesthesia) as predictors of complications. A multivariate model was then calculated using these predictors. RESULTS: Overall, 197 patients were included. The mean age was 72 (standard deviation ± 10). In total, 26.9% patients had at least 1 complication. Using univariate analysis, we found that each score significantly predicted complications. In multivariate analysis, only the ASA (odds ration [OR] 2.11; 95% confidence interval [CI] 1.01-4.43) and the NRS (OR 2.42; 95% CI 1.56-3.74) remained independent predictors. The best model incorporated ASA, NRS and gender, and predicted morbidity with an area under the curve of 76%. Our study's main limitations are population heterogeneity and limited sample size. CONCLUSION: The ASA and the NRS are important and independent determinants of early morbidity after transurethral procedures. The use of these indices may assist clinicians in the decision-making process to balance the possible benefits of transurethral procedures with the potential risks.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".