Psychometric properties of the global rating of change scales in patients with neck disorders: a systematic review with meta-analysis and meta-regression
Bibliographic record
Abstract
OBJECTIVE: The purpose of this systematic review was to critically appraise and synthesise the psychometric properties of Global Rating of Change (GROC) scales for assessment of patients with neck pain. DESIGN: Systematic review. DATA SOURCES: A search was performed in four databases (MEDLINE, EMBASE, CINAHL, SCOPUS) until February 2019. DATA EXTRACTION AND SYNTHESIS: Eligible articles were appraised using Consensus-based Standards for the selection of health Measurement Instruments checklist and the Quality Appraisal for Clinical Measurement Research Reports Evaluation Form. RESULTS: The search obtained 16 eligible studies and included in total 1533 patients with neck pain. Test-retest reliability of global perceived effect (GPE) was very high (intraclass correlation coefficient=0.80 to 0.92) for patients with whiplash. Pooled data of Pearson's r indicated that GROC scores were moderately correlated with neck disability change scores (0.53, 95% CI: 0.47 to 0.59). Pooled data of Spearman's correlations indicated that GROC scores were moderately correlated with neck disability change scores (0.56, 95% CI: 0.41 to 0.68). CONCLUSIONS: This study found excellent quality evidence of very good-to-excellent test-retest reliability of GPE for patients with whiplash-associated disorders. Evidence from very good-to-excellent quality studies found that GROC scores are moderately correlated to an external criterion patient-reported outcome measure evaluated pre-post treatment in patients with neck pain. No studies were found that addressed the optimal form of GROC scales for patients with neck disorders or compared the GROC to other options for single-item global assessment. PROSPERO REGISTRATION NUMBER: CRD42018117874.
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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 | 0.072 | 0.163 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.057 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.002 |
| 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".