Bibliographic record
Abstract
INTRODUCTION: Nonagenarians represent a rapidly growing patient population in Canada and have unique health concerns. With the goal of preparing urologists to manage this complicated patient population in the future, we sought to characterize referral patterns, diagnoses, investigations, treatments, and associated complications in a cohort of nonagenarians. Our second goal was to review anticholinergic burden (ACB) and rates of anticoagulation in this patient population and to assess the risk of hematuria in those who were anticoagulated. METHODS: This was a single-center, retrospective chart review of a sample of nonagenarians referred to our tertiary care center between 2009 and 2017. Demographic information, referral patterns, investigations, treatment plans, and outcomes were assessed. We assessed medication lists to calculate ACB scores at the time of referral, in addition to rates of anticoagulation use. RESULTS: Data was collected for 154 nonagenarians. Hematuria was the most common reason for referral (n=43, 27.9%). Urinary retention and lower urinary tract symptoms (LUTS) were seen in 22 and 36 patients, respectively. The majority of patients underwent routine investigations; however, treatment decisions were frequently based on age and frailty. Mild, moderate and severe ACB scores were seen in 76.6%, 9.33%, and 14.0% of patients, respectively. Of those referred for hematuria, 78.1% were on anticoagulation therapy. CONCLUSIONS: The most common reasons for urologic referral of nonagenarians include hematuria and LUTS. Most nonagenarians are offered routine investigations, and many are offered minor interventions for common benign and malignant urologic diagnoses. When treating nonagenarians, an individualized patient-centered care approach is likely most appropriate.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".