Bladder management is the top health concern among adults with a spinal cord injury
Bibliographic record
Abstract
INTRODUCTION AND OBJECTIVE: Individuals with spinal cord injury (SCI) commonly experience secondary complications though it is not known how they prioritize these different health domains. Using the Neurogenic Bladder Research Group (NBRG) SCI registry, our objective was to identify the top health concerns of individuals with SCI and identify factors that may be associated with these choices with particular focus on urologic issues that participants face. METHODS: Participants in the NBRG registry were asked: "What are the top 3 problems that affect you on a daily basis?" Urinary symptoms and QoL were assessed with the Neurogenic Bladder Symptom Score (NBSS). Multivariate regression was used to identify factors related to selecting a top ranked health issue. RESULTS: Among our 1461 participants, 882 (60.4%) were men and the median age was 45.1 years (IQR 25.3-64.9). Bladder management was the most commonly top ranked primary issue (39%) followed by pain (16.4%) and bowel management (11.6%). Factors associated with ranking bladder management as the primary concern included years since injury (OR 1.01 [1.00-1.02], p = 0.042), higher (worse) total NBSS (OR 1.05 [1.03-1.06], p < 0.001), and higher (worse) NBSS QoL (OR 1.25 [1.12-1.41], p < 0.001). Reporting chronic pain on a daily basis was associated with ranking pain as the primary health concern (OR 41.7 [15.7-170], p < 0.001). CONCLUSIONS: In this cohort, bladder management was ranked as the top health issue and increasing time from injury was associated with increased concern over bladder management. More bladder symptoms were also associated with ranking bladder management as a primary concern while bladder management method and urinary tract infections rate were not.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".