Identifying and Classifying Quality of Life Tools for Assessing Spasticity After Spinal Cord Injury
Bibliographic record
Abstract
OBJECTIVE: To identify and classify tools for assessing the influence of spasticity on quality of life (QOL) after spinal cord injury (SCI). METHODS: Electronic databases (MEDLINE/PubMed CINAHL and PsycInfo) were searched for studies published between 1975 and 2012. Dijkers's theoretical framework on QOL was used to classify tools as either objective or subjective measures of QOL. RESULTS: Sixteen studies met the inclusion criteria. Identified objective measures that were used to assess the influence of spasticity on QOL included the Short Form-36 (SF-36) the Sickness Impact Profile (SIP) and the Health Utilities Index-III (HUI-III). Subjective measures included the Quality of Life Index-SCI Version III (QLI-SCI) Life Situation Questionnaire-Revised (LSQ-R) Reciprocal Support Scale (RSS) Profile of Mood States (POMS) Spinal Cord Injury Spasticity Evaluation Tool (SCI-SET) and the Patient Reported Impact of Spasticity Measure (PRISM). A number of tools proved either to be insensitive to the presence of spasticity (QLI-SCI) or yielded mixed (SF-36) or weak (RSS LSQ-R) results. Tools that were sensitive to spasticity had limited psychometric data for use in the SCI population (HUI-III SIP POMS) although 2 were developed specifically for assessing spasticity on daily life post SCI (SCI-SET PRISM). CONCLUSIONS: Two condition-specific subjective measures the SCI-SET and PRISM emerged as the most promising tools for the assessment of spasticity impact on QOL after SCI. Further research should focus on establishing the psychometric properties of these measures for use in the SCI population.Key words: outcome measurement quality of life spasticity spinal cord injury.
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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 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.026 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".