A Patient-Centric Tool to Facilitate Goal Attainment Scaling in Neurogenic Bladder and Bowel Dysfunction: Path to Individualization
Bibliographic record
Abstract
OBJECTIVES: People with neurogenic bladder and/or bowel dysfunction experience diverse challenges that can be difficult to evaluate with standardized outcome measures. Goal attainment scaling (GAS) is an individualized, patient-centric outcome measure that enables patients/caregivers to identify and track their own treatment goals. Because creating goals de novo can be cumbersome, we aimed to develop a neurogenic bladder/bowel dysfunction goal menu to facilitate goal attainment scaling uptake and use. METHODS: We conducted a workshop with 6 expert clinicians to develop an initial menu. Individual interviews with 12 people living with neurogenic bladder and/or bowel dysfunction and 2 clinician panels with 5 additional experts aided us in refining the menu. A thematic framework analysis identified emergent themes for analysis and reporting. RESULTS: Interview participants were adults (median = 36 years, range 25-58), most with spinal cord injury (75%; 9/12). Of 24 goals identified initially, 2 (8%) were not endorsed and were removed, and 3 goals were added. Most participants listed "Impact on Life" goals (eg, Exercise, Emotional Well-Being) among their 5 most important goals (58%; 35/60). Three main themes emerged: challenges posed by incontinence, limitations on everyday life, and need for personalized care. CONCLUSIONS: We developed a clinical outcome assessment tool following a multistep process of representative stakeholder engagement. This patient-centric tool consists of 25 goals specific to people living with neurogenic bladder and/or bowel dysfunction. Asking people what matters most to them can identify important constructs that clinicians might have overlooked.
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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 | 0.043 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".