Reasons for cessation of clean intermittent catheterization after spinal cord injury: Results from the Neurogenic Bladder Research Group spinal cord injury registry
Bibliographic record
Abstract
INTRODUCTION: Clean intermittent catheterization (CIC) is recommended for bladder management after spinal cord injury (SCI) since it has the lowest complication rate. However, transitions from CIC to other less optimal strategies, such as indwelling catheters (IDCs) are common. In individuals with SCI who stopped CIC, we sought to determine how individual characteristics affect the bladder-related quality of life (QoL) and the reasons for CIC cessation. METHODS: The Neurogenic Bladder Research Group registry is an observational study, evaluating neurogenic bladder-related QoL after SCI. From 1479 participants, those using IDC or urinary conduit were asked if they had ever performed CIC, for how long, and why they stopped CIC. Multivariable regression, among participants discontinuing CIC, established associations between demographics, injury characteristics, and SCI complications with bladder-related QoL. RESULTS: There were 176 participants who had discontinued CIC; 66 (38%) were paraplegic and 110 (63%) were male. The most common reasons for CIC cessation among all participants were inconvenience, urinary leakage, and too many urine infections. Paraplegic participants who discontinued CIC had higher mean age, better fine motor scores, and lower educational attainment and employment. Multivariable regression revealed years since SCI was associated with worse bladder symptoms (neurogenic bladder symptom score), ≥4 urinary tract infections (UTIs) in a year was associated with worse satisfaction and feelings about bladder symptoms (SCI-QoL difficulties), while tetraplegia was associated better satisfaction and feelings about bladder symptoms (SCI-QoL difficulties). CONCLUSIONS: Tetraplegics who have discontinued CIC have an improved QoL compared with paraplegics. SCI individuals who have discontinued CIC and have recurrent UTIs have worse QoL.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".