Bridging the gap between statistical significance and clinical relevance: A systematic review of minimum clinically important difference (MCID) thresholds of scales reported in movement disorders research
Bibliographic record
Abstract
Background: MCID) is the smallest change in an outcome measure that is considered clinically meaningful. Using validated MCID thresholds for outcomes powers trials adequately to detect meaningful treatment effects, aids in their interpretation and guides development of new outcome measures. Objectives: To provide a comprehensive summary of MCID thresholds of various symptom severity scales reported in movement disorder. Methods: We conducted systematic review of the literature and included studies of one or more movement disorders, and reporting MCID scales. Results: 2763 reports were screened. Final review included 32 studies. Risk of bias (RoB) assessment showed most studies were of good quality. Most commonly evaluated scale was Unified Parkinson's Disease Rating Scale (UPDRS) (11 out of 32). Four studies assessing MDS-UPDRS had assessed its different sub-parts, reporting a change of 2.64,3.05,3.25 and 0.9 points to detect clinically meaningful improvement and 2.45,2.51,4.63 and 0.8 points to detect clinically meaningful worsening, for the Part I, II, III and IV, respectively. For Parts II + III, I + II + III and I + II + III + IV, MCID thresholds reported for clinically meaningful improvement were 5.73, 4.9, 6.7 and 7.1 points respectively; while those for clinically meaningful worsening were 4.7, 4.2, 5.2 and 6.3 points, respectively. MCID thresholds reported for other scales included Abnormal Involuntary Movement Scale (AIMS), Toronto Western Spasmodic Torticollis Rating Scale (TWSRS), and Burke-Fahn-Marsden Dystonia Scale (BFMD). Conclusion: This review summarizes all the MCID thresholds currently reported in Movement disorders research and provides a comprehensive resource for future trials, highlighting the need for standardized and validated MCID scales in movement disorder research.
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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 | 0.101 | 0.331 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.014 |
| Bibliometrics | 0.019 | 0.014 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| 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".