A review and evaluation of patient-reported outcome measures for spasticity in persons with spinal cord damage: Recommendations from the Ability Network – an international initiative
Bibliographic record
Abstract
Context: Patient-reported outcome measures (PROMs) are valuable for capturing the impact of spasticity on health-related quality of life (HRQoL) in persons with spinal cord damage (SCD) and evaluating the efficacy of interventions.Objective: To provide practical guidance for measuring HRQoL in persons with spasticity following SCD.Methods: Literature reviews identified measures of HRQoL and caregiver burden, utilized in studies addressing spasticity in SCD. Identified measures were evaluated for clinical relevance and practicality for use in clinical practice and research. The PRISM, SCI-SET, EQ-5D and SF-36 instruments were mapped to the International Classification of Functioning, Disability and Health (ICF). The PRISM and SCI-SET were evaluated using the Consensus-based Standards for the Selection of Health Measurement Instruments (COSMIN) checklist.Results: Two spasticity-specific, five generic, and four preference-based measures were identified. ICF mapping and the COSMIN checklist supported the use of the PRISM and SCI-SET in SCD. The SF-36 is considered the most useful generic measure; disability-adapted versions may be more acceptable but further studies on psychometric properties are required. The SF-36 can be converted to a preference-based measure (SF-6D), or alternatively the EQ-5D can be used. While no measures specific to caregivers of people with SCD were identified, the Caregiver Burden Scale and the Zarit Burden Interview are considered suitable.Conclusion: Recommended measures include the PRISM and SCI-SET (condition-specific), SF-36 (generic), and Caregiver Burden Scale and Zarit Burden Interview (caregiver burden). Consideration should be given to using condition-specific and generic measures in combination; the PRISM or SCI-SET combined with SF-36 is recommended.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".