What can we learn from the relationship between gait deviations and clinical impairments when comparing two databases?
Bibliographic record
Abstract
BACKGROUND: Several previous studies have tried to determine the relationship between gait and clinical impairments in children with Cerebral Palsy (CP). The heterogeneity of the population and the methodology used could explain the discrepancy within the results. Recently, Papageorgiou et al. (2019) used a Statistical Parametric Mapping (SPM) analysis to investigate this relationship, allowing to test across the kinematic waveforms parameters. RESEARCH QUESTION: Are we able to replicate the results of Papageorgiou et al. (2019) on a population of children with CP from another center? METHODS: Retrospectively, youth with spastic unilateral (uCP) or bilateral (bCP) CP (3-18 years of age) who underwent a clinical gait analysis at the Geneva University Hospitals (HUG) were screened. Following Papageorgiou et al. 2019, the same inclusion and exclusion criteria as well as the same methodology were applied. Mann-Whitney-U test was used to compare the impairments score between the two centers. A Student T-Test using SPM was applied to compare the kinematic waveforms from the two centers. A canonical correlation analysis using SPM was realized to assess the relationship between clinical impairments and the combined sagittal motion of the pelvis, hip, knee and ankle. RESULTS AND SIGNIFICANCE: A total of 211 patients were included with 131 uCP (10 [8-14] years old) and 80 bCP (11 [7-14] years old). The distribution of the Gross Motor Function Classification System levels and the proportion of previous treatment differs between centers. In both CP groups, significant differences were observed in the composite score and lower limb kinematics, reflecting less impaired patients with CP at HUG compared to Papagergiou et al. (2019). While similar associations between spasticity and kinematic were observed in both centers, the association with muscle weakness, selectivity, and range of motion differed.
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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 | 0.056 | 0.236 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".