Can a Simple Geriatric Assessment Predict the Outcome of TURP?
Bibliographic record
Abstract
PURPOSE: To determine the impact of a simple preoperative geriatric assessment on the outcome in older patients with recurrent urinary retention who underwent desobstructive surgery. PATIENTS AND METHODS: Patients aged 75 years or older with recurrent urinary retention referred for TURP entered this prospective, multicentre study. Several demographic, intra- and postoperative parameters were assessed. Preoperative geriatric assessment was performed by the 7-item Canadian Study of Health and Ageing (CSHA) frailty scale (1: very fit, 7: severely frail; completion takes less than a minute). The main outcome parameters were successful voiding rates at discharge and 3 months postoperatively. RESULTS: A total of 54 patients were recruited; 42 (77.8%) patients had a CSHA index of 1-3 and were considered as "fit", the remaining 12 (22.2%) formed the "frail" group (CSHA index 4-7). Age was identical in both cohorts (79.5 ± 3.7 vs. 79.7 ± 3.3 years); differences were demonstrable for the American Society of Anesthesiologists (ASA) score (p = 0.001), the number of daily medications (>4: 32 vs. 75%, p = 0.02), falls within the past 6 months (12 vs. 33%), and the necessity of home/nursing care (5 vs. 42%, p = 0.004). Intra- and perioperative complications, duration of postoperative catheterization, and length of hospitalization were identical in both cohorts. The success rate at discharge was 80.6% in fit and 75.0% in frail patients; the respective values at 3 months were 95.2 and 83.3%. CONCLUSIONS: A simple 1-min geriatric assessment tool can predict - to a certain extent - the outcome of desobstructive surgery in older patients with recurrent urinary retention. Fit patients achieve an excellent outcome while frail patients might benefit from a more in-depth urodynamic/geriatric evaluation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".