Development of a Comprehensive Outcome Measure for Motor Coordination; Step 1: Three-Phase Content Validity Process
Bibliographic record
Abstract
BACKGROUND: Motor coordination, the ability to produce context-dependent organized movements in spatial and temporal domains, is impaired after neurological injuries. Outcome measures assessing coordination mostly quantify endpoint performance variables (ie, temporal qualities of whole arm movement) but not movement quality (ie, trunk and arm joint displacements). OBJECTIVE: To develop an outcome measure to assess coordination of multiple body segments at both endpoint trajectory and movement quality levels, based on observational kinematics, in adults with neurological injuries. METHODS: A 3-phase study was used to develop the Comprehensive Coordination Scale (CCS): instrument development, Delphi process, and focus group meeting. The CCS was constructed from common tests used in clinical practice and research. Rating scales for different behavioral elements were developed to guide analysis. For content validation, 8 experts (ie, neurological clinicians/researchers) answered questionnaires about relevance, comprehension, and feasibility of each test and rating scale. A focus group conducted with 6 of 8 experts obtained consensus on rating scale and instruction wording, and identified gaps. Three additional experts reviewed the revised CCS content to obtain a final version. RESULTS: Experts identified a gap regarding assessment of hand/finger coordination. The CCS final version is composed of 6 complementary tests of coordination: finger-to-nose, arm-trunk, finger, lower extremity, and 2- and 4-limb interlimb coordination. Constructs include spatial and temporal variables totaling 69 points. Higher scores indicate better performance. CONCLUSIONS: The CCS may be an important, understandable and feasible outcome measure to assess spatial and temporal coordination. CCS measurement properties are presented in the companion article.
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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 | 0.078 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".