A Prospective Evaluation of Different Frailty Indices in Patients Undergoing Transurethral Resection of Bladder Tumor
Bibliographic record
Abstract
BackgroundMost studies investigating the relationship between preoperative frailty and postoperative outcomes among bladder cancer patients only assess frailty retrospectively in patients who have undergone radical cystectomy. Transurethral resection of bladder tumor (TURBT) is a commonly performed procedure in outpatient settings for a large number of bladder cancer patients. The prevalence of frailty among bladder cancer patients and its impact on postoperative complications and mortality are not well studied. MethodsTo assess the prevalence of frailty among bladder cancer patients planned for TURBT at a tertiary cancer center using the modified frailty index (mFI) and Risk Analysis Index (RAI) and further assess the impact of these indices on 30-day postoperative complications and mortality rates. ResultsBetween May 2020 and March 2021, 343 consecutive patients were enrolled. The mean age of the cohort was 64.8 ± 13.1 years, 86.6% were male, and 82% had non–muscle-invasive bladder cancer (NMIBC). The majority of the cohort (92%) was found to have low American Society of Anesthesiologists (ASA) score class (I + II), while 35.3% were labeled as frail using mFI 2+, and 32.1% based on RAI (III, IV). The 30-day readmission, postoperative complications, and mortality rates in this cohort were 3.8%, 2.3%, and 6.6%, respectively. RAI was a better indicator of mortality compared to mFI. As such, patients with low RAI score (I, II) had 0.054 odds for 30-day mortality compared to the patients with high RAI score (III, IV) (OR 0.054; CI 95%, 0.004 to 0.784; P = 0.033).Conclusion Frailty, as measured by Risk Analysis Index, is an independent predictor of early mortality in patients undergoing TURBT. Preoperative frailty assessment may improve risk stratification and patient counseling prior to surgery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".