Frailty and elderly in urology: Is there an impact on post-operative complications?
Bibliographic record
Abstract
INTRODUCTION: Frailty used as predictive tool is still not carried out in daily practice, although many studies confirm the great clinical importance of the frailty syndrome in surgical outcomes. There is no standardized method of measuring the physiological reserves of older surgical patients. The aim of this study was to analyze a cohort of older urological patients according to various frailty indices, in order to evaluate whether they are predictors of post-operative complications after urological procedures. MATERIAL AND METHODS: This is a prospective observational study on 78 consecutive older (≥70 years) patients, subjected to major urological (both endoscopic and 'open surgical') procedures. Frailty was defined according to the Edmonton Frail Scale. Several risk models and biochemical parameters were evaluated. Post-operative outcomes were surgical and medical complications, mortality and rehospitalisation within 3 months. RESULTS: An overall prevalence of frailty of 21.8% was found. Patients with complications were frailer than those without complications (univariate analysis), considering both total patients (p = 0.002) and endoscopic (p = 0.04) and 'open surgical' patients (p = 0.013). However, in multivariate analysis, a significant correlation was not found between all frailty indices tested and the risk of major complications. Limitation of the study: the small sample size (lack of statistical power), although this is a prospective study focused on older urological patients. CONCLUSIONS: New urology-tailored pre-operative assessment tools may prove beneficial when calculating the risks/benefits of urological procedures, so that objective data can guide surgical decision- making and patient counselling. Further large clinical studies specifically focusing on elderly in urology will be needed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".