Recommendations for evaluation of neurogenic bladder and bowel dysfunction after spinal cord injury and/or disease
Bibliographic record
Abstract
Objective: To provide an overview of clinical assessments and diagnostic tools, self-report measures (SRMs) and data sets used in neurogenic bladder and bowel (NBB) dysfunction and recommendations for their use with persons with spinal cord injury /disease (SCI/D).Methods: Experts in SCI/D conducted literature reviews, compiled a list of NBB related assessments and measures, reviewed their psychometric properties, discussed their use in SCI/D and issued recommendations for the National Institutes of Health (NIH), National Institute of Neurological Disorders and Stroke (NINDS) Common Data Elements (CDEs) guidelines.Results: Clinical assessments included 15 objective tests and diagnostic tools for neurogenic bladder and 12 for neurogenic bowel. Following a two-phase evaluation, eight SRMs were selected for final review with the Qualiveen and Short-Form (SF) Qualiveen and the Neurogenic Bowel Dysfunction Score (NBDS) being recommended as supplemental, highly-recommended due to their strong psychometrics and extensive use in SCI/D. Two datasets and other SRM measures were recommended as supplemental.Conclusion: There is no one single measure that can be used to assess NBB dysfunction across all clinical research studies. Clinical and diagnostic tools are here recommended based on specific medical needs of the person with SCI/D. Following the CDE for SCI studies guidelines, we recommend both the SF-Qualiveen for bladder and the NBDS for bowel as relatively short measures with strong psychometrics. Other measures are also recommended. A combination of assessment tools (objective and subjective) to be used jointly across the spectrum of care seems critical to best capture changes related to NBB and develop better treatments.
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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 | 0.045 | 0.187 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.016 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".