Variability and reproducibility of gait parameters in youth with cerebral palsy: Feasibility for multicenter motion analysis studies
Bibliographic record
Abstract
BACKGROUND: Demonstrating instrumented gait analysis inter-evaluator reproducibility is essential to perform proper multicenter studies. Studies have evaluated inter-evaluator reproducibility but often were limited to highly experienced evaluators or assessed in healthy individuals only. The current study aimed at determining if introducing variability through evaluators with various years of gait analysis experience would lead to acceptable levels of reproducibility? METHODS: Three adolescent and one young adult with cerebral palsy were each evaluated by four out of ten evaluators with various years of marker-based gait analysis experience. Gait analysis was performed on a 10-m walkway at patients' preferred speed. The intraclass correlation coefficient (ICC), and the intrinsic (inter-trial) and extrinsic (inter-evaluator) variability of gait parameters were computed. FINDINGS: Ten evaluators from nine different motion analysis centers (average of 8.4 ± 10.4 years of gait analysis experience; min: 1 year; max: 33 years) participated in the study. For most joints, good to excellent (0.75 to 1 ICCs) reproducibility was reported. Error analysis revealed that the main source of variability was associated with evaluators and not patients' gait. Regression analysis showed that years of experience in a motion analysis center was not a significant predictor of mean inter-evaluator deviation (β= -0.002 ± 0.006; p = 0.659) or of its standard deviation (β= -0.002 ± 0.004; p = 0.650). INTERPRETATION: The result of the current project suggests that one year of marker-based gait analysis experience and reviewing of a procedure video prior to engaging in a study is sufficient to generate quality data.
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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 | 0.061 | 0.104 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".