Characterizing Urine and Sediment in Individuals with Lower Urinary Tract Dysfunction Utilizing Intermittent Catheters
Bibliographic record
Abstract
Background/Objectives: To perform a physicochemical characterization of urine and sediment in intermittent catheterization (IC) users and evaluate the impact of IC with micro-hole zone catheters (MHZC) and conventional two-eyelet catheters (CEC). Methods: Analysis of anonymized urine samples collected from four IC user groups with lower urinary tract dysfunction (LUTD): Newly diagnosed individuals with spinal cord injury (SCI) from an inpatient SCI clinic (A), and community-based IC users with SCI (B), multiple sclerosis (MS) (C), or other conditions than SCI or MS (D). Urine analysis included physicochemical properties, bacterial load, and sediment size, both after collection and following passage through MHZC and CEC. Results: Urine samples from 53 participants were analyzed (groups A: 11, B: 11, C: 9, D: 22). The physicochemical properties of urine were similar to reference values despite the prevalence of bacteriuria ranging from 54.5% to 77.3%. The median [99th percentile] sediment size in the total group was 8.6 µm [50.7 µm] and 8.5 µm [54.1 µm] for group A, 9.2 µm [40.3 µm] for group B, 7.9 µm [48.3 µm] for group C, and 8.9 µm [50.3 µm] for group D. Following catheter passage, the median sediment size for the total group was 8.9 µm with the MHZC and 8.9 µm with the CEC. Conclusions: This two-part study initially presented a novel approach to characterizing urine samples, including sediment from IC users, and, thereafter, an in vitro experiment using the samples to test sediment passage through MHZC and CEC. The results indicated similar urine properties and sediment sizes across groups and did not suggest differences or issues relating to urine and sediment passage through these IC technologies for these groups.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".