Specificity of manual testing of the gluteal muscle strength in preschool children: a single-center cross-sectional study
Bibliographic record
Abstract
Background: Manual Muscle Testing (MMT) is a procedure used to assess the strength and function of individual muscles and muscle groups based on effective movement in relation to the forces of gravity and manual resistance.The introduction of MMT into the diagnosis of developmental age is a complex issue due to the challenge of obtaining cooperation from the child undergoing the test.While some authors have attempted to implement MMT in children, clear recommendations regarding the lower age limit have not been firmly established.Aims: This study aimed to establish the criteria for conducting MMT in healthy preschool children, identify standard protocols for test administration, and assess the potential influence of biometric parameters (such as age, gender, height, and weight) on test performance. Material and methods:A group of 111 children underwent observation and was divided into two age groups.Group I comprised children aged from the completion of the 3rd year to the beginning of the 5th year (n=47; 24 girls, 23 boys; mean age 4.06±0.42years, body weight 17.71±2.81kg, and body height 104.9±6.50 cm).Group II included children aged from the completion of the 5th year to the beginning of the 7th year (n=54; 23 girls and 31 boys; mean age 5.79±0.60years, body weight 21.6±3.68kg, and body height 116.6 ±7.59 cm).The standard MMT position was utilized for assessment. 65
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".