Spinal cord injury and quality of life: a systematic review of outcome measures
Bibliographic record
Abstract
STUDY DESIGN: Systematic review. STUDY RATIONALE: According to current estimates, there are more than 1 million people living with a spinal cord injury (SCI) in the United States alone. Given the potentially devastating impact of SCI on health-related quality of life (QoL), we sought to gain an improved understanding of QoL outcomes in SCI. OBJECTIVE: To identify and describe common QoL outcomes measures in patients with SCI. METHODS: A systematic review of the English-language literature was undertaken for articles published from 1998 through December 2010. Electronic databases and reference lists of key articles were searched to identify measures or indices used to evaluate QoL outcomes in patients with SCI. The titles and abstracts of the SCI peer-reviewed literature were searched to determine which of these outcome measures were most commonly used to evaluate QoL in patients with SCI. RESULTS: We identified 27 outcome measures used to evaluate QoL in patients with SCI. In SCI literature, the six most commonly used objective outcome measures were the Short-form 36 (SF-36); Craig Handicap Assessment and Reporting Technique (CHART); Short-form 12 (SF-12); Sickness Impact Profile (SIP68); Reintegration to Normal Living Index (RNL); and Community Integration Questionnaire (CIQ). The six subjective measures that were most frequently used were the Satisfaction with Life Scale (SWLS); Quality of Life Index (QLI); Life Satisfaction Questionnaire (LISAT-9/-1); World Health Organization Quality of Life-BREF scale (WHOQOL-BREF); Perceived Quality of Life (PQOL); and global QoL. All six objective measures have been validated in an SCI population, and four of the six subjective measures have been similarly validated. Three of each of the objective and subjective measures have been reliability tested in a population with SCI. CONCLUSION: In addition to neurological and functional changes after SCI, QoL outcomes should be routinely assessed. Choice of appropriate QoL measure should be influenced by the study objectives and design, as well as the psychometric properties of the particular measure within the context of SCI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".