The creation and validation of a short form of the Neurogenic Bladder Symptom Score
Bibliographic record
Abstract
AIM: To develop a short form (SF) of the 24-item Neurogenic Bladder Symptom Score (NBSS). METHODS: We used three previously published datasets. First, we selected the most responsive questions within each of the domains. Internal validity of the NBSS-SF was assessed using Cronbach's α. External validity was assessed by evaluating hypothesized relationships with other questionnaires and testing correlations with the full NBSS domains. Test-retest reliability of the NBSS-SF domains was determined using an intraclass coefficient (ICC). RESULTS: Using data from a prior responsiveness study, we selected questions for the NBSS-SF from the incontinence domain (three), storage/voiding domain (three), consequences domain (two); these would make up the NBSS-SF. We used the original NBSS validation cohort of 230 patients with multiple sclerosis (MS), spinal cord injury (SCI), or spina bifida, and found the Cronbach's α was .76 for the NBSS-SF; the external validity was high, with correlations between specific NBSS-SF domains/total scores and the Qualiveen-SF, ICIQ, and AUASS generally similar to those seen with the NBSS. Correlations between the NBSS-SF domains and the full NBSS domains were high. The NBSS-SF ICC in a subset of 120 patients was 0.84. The NBSS-SF performed similarly in two additional independent datasets. CONCLUSIONS: The total score of the NBSS-SF has appropriate validity, reliability, and could be used instead of the full NBSS to minimize the assessment burden. The full NBSS may be better suited if the primary focus of the study is on neurogenic bladder symptoms, or if individual NBSS domains are of interest.
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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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".