The psychometric properties of the Quality of Life in Neurological Disorders (Neuro-QoL) measurement system in neurorehabilitation populations: a systematic review
Bibliographic record
Abstract
OBJECTIVE: To systematically review the literature of existing evidence on the measurement properties of the Quality of Life in Neurological Disorders (Neuro-QoL) measurement system among neurorehabilitation populations. DATA SOURCES: The Consensus-based Standards for the selection of health Measurement Instruments (COSMIN) guided this systematic review in which we searched nine electronic databases and registries, and hand-searched reference lists of included articles. STUDY SELECTION: Two independent reviewers screened selected articles and extracted data from 28 included studies. DATA EXTRACTION: COSMIN's approach guided extraction and synthesizing measurement properties evidence (insufficient, sufficient), and the modified GRADE approach guided synthesizing evidence quality (very-low, low, moderate, high) by diagnosis. DATA SYNTHESIS: Neuro-QoL has sufficient measurement properties when used by individuals with Huntington's disease, Multiple Sclerosis, Parkinson's disease, stroke, lupus, cognitive decline, and amyotrophic lateral sclerosis. The strongest evidence is for the first four conditions, where test-retest reliability, construct validity, and responsiveness are nearly always sufficient (GRADE: moderate-high). Structural validity is assessed only in multiple sclerosis and stroke but is often insufficient (GRADE: moderate-high). Criterion validity is sufficient in some stroke and Huntington's disease domains (GRADE: high). Item response theory analyses were reported for some stroke domains only. There is limited, mixed evidence for responsiveness and measurement error (GRADE: moderate-high), and no cross-cultural validity evidence CONCLUSIONS: Neuro-QoL domains can describe and evaluate patients with Huntington's disease, multiple sclerosis, Parkinson's disease, and stroke, but predictive validity evidence would be beneficial. In the other conditions captured in this review, a limited number of Neuro-QoL domains have evidence for descriptive use only. For these conditions, further evidence of structural validity, measurement error, cross-cultural validity and predictive validity would enhance the use and interpretation of Neuro-QoL.
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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 | 0.195 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 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".