Bibliographic record
Abstract
Many interventions for neurogenic bladder patients are directed towards improving quality of life (QOL). Patient reported outcome measures (PROMs) are the primary method of evaluating QOL, and they provide an important quantification of symptoms which can't be measured objectively. Our goal was to review general measurement principles, and identify and discuss PROMs relevant to neurogenic bladder patients. We identify two recent reviews of the state of the literature and updated the results with an additional Medline search up to September 1, 2015. Using the previous identified reviews, and our updated literature review, we identified 16 PROMs which are used for the assessment of QOL and symptoms in neurogenic bladder patients. Several are specifically designed for neurogenic bladder patients, such as the Qualiveen (for neurogenic bladder related QOL), and the Neurogenic Bladder Symptom Score (NBSS) (for neurogenic bladder symptoms). We also highlight general QOL measures for patients with multiple sclerosis (MS) and spinal cord injury (SCI) which include questions about bladder symptoms, and incontinence PROMs which are commonly used, but not specifically designed for neurogenic bladder patients. It is essential for clinicians and researchers with an interest in neurogenic bladder to be aware of the current PROMs, and to have a basic understanding of the principals of measurement in order to select the most appropriate one for their purpose.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".