Measurement properties of the Traumatic Brain Injury Quality of Life (TBI-QoL) and Spinal Cord Injury Quality of Life (SCI-QoL) measurement systems: a systematic review
Bibliographic record
Abstract
PURPOSE: Traumatic brain injury and spinal cord injury impact all areas of individuals' quality of life. A synthesis of available evidence for the Traumatic Brain Injury Quality of Life (TBI-QoL) and Spinal Cord Injury Quality of Life (SCI-QoL) measurement systems could inform evidence-based clinical practice and research. Thus, we aimed to systematically review the literature of existing evidence on the measurement properties of SCI-QoL and TBI-QoL among rehabilitation populations. METHODS: We used the COnsensus-based Standards for the selection of health Measurement Instruments (COSMIN) framework for evaluating measures to guide this systematic review. We searched nine electronic databases and registries, and hand-searched reference lists of included articles. Two independent reviewers screened selected articles and extracted the data. We used COSMIN's thresholds to synthesize measurement properties evidence (insufficient, sufficient), and the modified GRADE approach to synthesize evidence quality (very-low, low, moderate, high). RESULTS: We included 16 studies for SCI-QoL and 14 studies for TBI-QoL. Both measurement systems have sufficient content validity, structural validity, internal consistency and construct validity across nearly all domains (GRADE: high). Most SCI-QoL domains and some TBI-QoL domains have sufficient evidence of cross-cultural validity and test-retest reliability (GRADE: moderate-high). Besides the cognition domains of TBI-QoL, which have indeterminate evidence for measurement error and sufficient evidence for responsiveness (GRADE: high), there is no additional evidence available for these measurement properties. CONCLUSION: Rehabilitation researchers and clinicians can use SCI-QoL and TBI-QoL to describe and evaluate patients. Further evidence of measurement error, responsiveness, and predictive validity would advance the use and interpretation of SCI-QoL and TBI-QoL in rehabilitation.
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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 | 0.047 | 0.208 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.011 |
| Bibliometrics | 0.022 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".