The conceptualization and development of a patient-reported neurogenic bladder symptom score
Bibliographic record
Abstract
BACKGROUND: There is no single patient-reported instrument that was developed specifically to assess symptoms and bladder-related consequences for neurogenic bladder dysfunction. The purpose of this study was to identify and consolidate items for a novel measurement tool for this population. METHODS: Item generation was based on a literature review of existing instruments, open-ended semistructured interviews with patients, and expert opinion. Judgment-based item reduction was performed by a multidisciplinary expert group. The proposed questionnaire was sent to external experts for review. RESULTS: Eight neurogenic quality of life measures and 29 urinary symptom-specific instruments were identified. From these, 266 relevant items were extracted and used in the creation of the new neurogenic symptom score. Qualitative interviews with 16 adult patients with neurogenic bladder dysfunction as a result of spinal cord injury, multiple sclerosis, or spina bifida were completed. Dominant themes included urinary incontinence, urinary tract infections, urgency, and bladder spasms. Using the literature review and interview data, 25 proposed items were reviewed by 12 external experts, and the questions evaluated based on importance on a scale of 1 (not important) to 5 (very important). Retained question domains had high mean importance ratings of 3.1 to 4.3 and good agreement with answer hierarchy. CONCLUSION: The proposed neurogenic bladder symptom score is a novel patient-reported outcome measure. Further work is underway to perform a data-based item reduction and to assess the validity and reliability of this instrument.
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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 | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".