Motor cognitive processing speed estimation among the primary schoolchildren by deriving prediction formula: A cross-sectional study
Bibliographic record
Abstract
ABSTRACT Objectives: Motor cognitive processing speed (MCPS) is often reported in terms of reaction time. In spite of being a significant indicator of function, behavior, and performance, MCPS is rarely used in clinics and schools to identify kids with slowed motor cognitive processing. The reason behind this is the lack of availability of convenient formula to estimate MCPS. Thereby, the aim of this study is to estimate the MCPS in the primary schoolchildren. Materials and Methods: Two hundred and four primary schoolchildren, aged 6–12 years, were recruited by the cluster sampling method for this cross-sectional study. MCPS was estimated by the ruler drop method (RDM). By this method, a metallic stainless steel ruler was suspended vertically such that 5 cm graduation of the lower was aligned between the web space of the child's hand, and the child was asked to catch the moving ruler as quickly as possible, once released from the examiner's hand. Distance the ruler traveled was recorded and converted into time, which is the MCPS. Multiple regression analysis of variables was performed to determine the influence of independent variables on MCPS. Results: Mean MCPS of the entire sample of 204 primary schoolchildren is 230.01 ms ± 26.5 standard deviation (95% confidence interval; 226.4–233.7 ms) that ranged from 162.9 to 321.6 ms. By stepwise regression analysis, we derived the regression equation, MCPS (ms) = 279.625–5.495 × age, with 41.3% (R = 0.413) predictability and 17.1% (R 2 = 0.171 and adjusted R 2 = 0.166) variability. Conclusion: MCPS prediction formula through RDM in the primary schoolchildren has been established.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".