Determining minimally clinically important differences for outcome measures in patients with chronic motor deficits secondary to traumatic brain injury
Bibliographic record
Abstract
OBJECTIVE: To determine minimally clinically important differences (MCIDs) for Disability Rating Scale (DRS), Fugl-Meyer Upper Extremity Subscale (FM-UE), Fugl-Meyer Lower Extremity Subscale (FM-LE), and Fugl-Meyer Motor Scale (FMMS) in patients with chronic motor deficits secondary to traumatic brain injury (TBI). METHODS: Retrospective analysis from the 1-year, double-blind, randomized, surgical sham-controlled, Phase 2 STEMTRA trial (NCT02416492), in which patients with chronic motor deficits secondary to TBI (N = 61) underwent intracerebral stereotactic implantation of modified bone marrow-derived mesenchymal stromal (SB623) cells. MCIDs for DRS, FM-UE, FM-LE, and FMMS were triangulated with distribution-based, anchor-based, and Delphi panel estimates. RESULTS: Triangulated MCIDs were: 1) -1.5 points for the Disability Rating Scale; 2) 6.2 points for the Fugl-Meyer Upper Extremity Subscale; 3) 3.2 points for the Fugl-Meyer Lower Extremity Subscale; and 4) 8.4 points for the Fugl-Meyer Motor Scale. CONCLUSIONS: For the first time in the setting of patients with chronic motor deficits secondary to TBI, this study reports triangulated MCIDs for: 1) DRS, a measure of global outcome; and 2) Fugl-Meyer Scales, measures of motor impairment. These findings guide the use of DRS and Fugl-Meyer Scales in the assessment of global disability outcome and motor impairment in future TBI clinical trials.
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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 | 0.016 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".