Assessment and comparison of cognitive function tests in abacus trained and untrained students aged 8-12 years in the South-Indian population
Bibliographic record
Abstract
Introduction and Aim: During childhood period, cognitive decline could manifest as learning disability. Cognitive abilities, which have a strong relationship with synaptic plasticity an amazing property of the brain is mandatory for the growing demands and challenges. Our study focuses on the effect of memory tasking (abacus training) on cognitive abilities in children. The aim of the study is to assess the cognitive functions of children who had undergone level one abacus training in the age group of 8-12 years and to compare the cognitive functions between children trained and untrained in abacus. Materials and Methods: The study was approved by ICMR-STS 2018 and institutional ethics committee. It was conducted in children aged 8-12 years, who were age and BMI matched and were untrained and trained in abacus level one, between June-July 2018. Seventy participants, majority being females (fourth-fifth standard), were recruited from a school in Puducherry. Anthropometric indices were recorded, and baseline cognitive parameters were assessed by MoCA. Results: The total median scores for MoCA were 28 (27-29) and 26 (23-28) in study and control groups respectively (p=0.016). Values for trail making test A was 44.89 sec (study group), 56.20 sec (control group) (p=0.04) and trail making test B was 94.7 sec (study group) and 125.3 sec (control group; p=0.03) respectively. In letter cancellation test, the scores were 31.8(study group) and 31 seconds (control group; p=0.019). Conclusion: Abacus training improves all three domains of cognition and learning abilities in children.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".