The Clinical Frailty Scale as a Predictor of Trial of Void Outcomes in Men Undergoing Transurethral Resection of Prostate Surgery
Bibliographic record
Abstract
BackgroundWith the growing elderly population, there is an increasing prevalence of frail patients undergoing surgeries. A common operation in this group is the transurethral resection of prostate (TURP) for the treatment of benign prostatic hyperplasia. Whilst there is a demonstrable link between frailty and general adverse postoperative outcomes, there is limited research on frailty and trial of void (TOV) outcomes post TURP.This study aims to investigate possible associations between frailty, TOV outcomes, and postoperative complications following a TURP.MethodsA retrospective review was conducted of adult patients treated with TURP at 2 hospitals from January 2018 to December 2019, inclusive. Patient demographic data, preoperative Clinical Frailty Scale scores, trial of void outcomes, and complications were recorded and analysed. Clinical frailty scores (CFS) were recorded in accordance with the Dalhousie University Clinical Frailty Scale, ranging from 1 (very fit) to 9 (terminally ill).ResultsA total of 226 patients (median age 70.5 years) were identified for this study. Of these patients, 59 were identified as having a CFS of 1 to 2 (Group A), 140 patients had a CFS of 3 to 4 (Group B), and 27 patients had a CFS of 5 to 7 (Group C). Within the initial TOV, Group C had a statistically significant difference in failure rates compared with the other 2 groups, with Group C having the highest failure rate of 33.3% (9/27), followed by Group B with 14.3% (20/140), and then Group A with 13.6 % (8/59) (P = 0.04).ConclusionIn conclusion, greater preoperative frailty is associated with higher rates of initial TOV failure in post-TURP patients. Early objective identification of elderly patients with increased frailty is useful to help preoperative counselling and decision-making, to manage patient postoperative expectations, and to optimise patient care.
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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 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".