How many observations in the reference dataset are required to compute a consistent Gait Deviation Index & Gait Profile Score?
Bibliographic record
Abstract
BACKGROUND: The Gait Deviation Index (GDI) and the Gait Profile Score (GPS) are the most used scores to sum up gait deviations and are used as primary outcomes in many clinical studies. They are considered as equivalent scores. The computation of these scores is based on a reference dataset but often no description is provided. Among other characteristics, the number of observations needed and its possible influence on the computation of the scores remains unknown. RESEARCH QUESTION: Define the number of observations needed in the reference dataset to compute consistent and reliable GDI and GPS. METHODS: Fifty individuals with cerebral palsy (CP) were randomly selected from our laboratory database. Both scores were computed based on the reference dataset of Schwartz et al. (2008). A bootstrap analysis was performed, for every individual, to assess the effect of the number of observations on both scores. N number of observations were randomly selected, with replacement, from the reference dataset. This procedure was repeated 2000 times for every individual and every N and performed from N = 5 to N = 165 with an increment of 5. The 95 % of the absolute error distribution was considered for every individual and every N. The smallest detectable change (SDC) for both scores was considered as a threshold (GDI: 10.8; GPS:1.3°) to determine the minimum N required. RESULTS AND SIGNIFICANCE: A minimum of 90 and 20 observations are required to compute consistent GDI and GPS, respectively. The number of observations has a higher impact on the GDI than the GPS, mainly because the GPS calculation does not rely on the standard deviation (SD). Furthermore, the GDI absolute error seems to be higher in individuals with greater gait deviations, i.e. lower GDI value. This effect was not observed on the GPS. In the case of a small reference dataset, the GPS should therefore be preferred.
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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 | 0.024 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".