Quantifying Effects of Dataset Size, Data Variability, and Data Curvature on Modelling of Simulated Age-Related Motor Development Data
Bibliographic record
Abstract
Abstract Motor development in children and youth occurs non-linearly; improvements are rapid at younger ages and decrease as they reach adulthood. There is also evidence that performance variability changes as children and youth age. Accurate models of typical performance are necessary to identify deficits in motor performance and to track the efficacy of therapies. Robotic devices have been used previously to measure motor performance in children and youth, and produce models of typical performance; however, power analyses on these models have not been explored. An algorithm was created to generate normative models of typical motor performance. The accuracy and repeatability of the algorithm were tested using simulated data that changed the number of data points, and the curvature and variability of the data. Two-hundred and eighty-eight participants who are typically developing (ages 5-18) completed a robotic point-to-point reaching task with the Kinarm Exoskeleton. Exponential curves were fit to reaction time measured by the Kinarm to model typical performance. The results of the simulations were used to generate confidence intervals on the models of typical performance. The simulations showed that number of datapoints had the largest impact on accuracy and repeatability of the models, and that repeatability was age-dependent. The simulations with the uniform and non-uniform datasets generated different confidence intervals; however, these differences were minimal when the number of datapoints at each age were matched between the two datasets. To ensure identification of deficits is accurately determined, there is a need to account for differences in repeatability when developing models of typical motor performance in children and youth. The results of our simulations can be used to assess repeatability of non-linear models of motor performance based on dataset size in the future.
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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 | 0.032 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".